Cited by 1,882Statistical Methods in EpidemiologyMeta-analysis and systematic reviewsClinical Laboratory Practices and Quality Control

The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.

Understanding diagnostic tests 3: receiver operating characteristic curves

Akobeng, Anthony K·2007·Acta Paediatrica

DOI 10.1111/j.1651-2227.2006.00178.x

Open this paper in CoChat — free account

Abstract

The results of many clinical tests are quantitative and are provided on a continuous scale. To help decide the presence or absence of disease, a cut‐off point for ‘normal’ or ‘abnormal’ is chosen. The sensitivity and specificity of a test vary according to the level that is chosen as the cut‐off point. The receiver operating characteristic (ROC) curve, a graphical technique for describing and comparing the accuracy of diagnostic tests, is obtained by plotting the sensitivity of a test on the y axis against 1‐specificity on the x axis. Two methods commonly used to establish the optimal cut‐off point include the point on the ROC curve closest to (0, 1) and the Youden index. The area under the ROC curve provides a measure of the overall performance of a diagnostic test. In this paper, the author explains how the ROC curve can be used to select optimal cut‐off points for a test result, to assess the diagnostic accuracy of a test, and to compare the usefulness of tests. Conclusion: The ROC curve is obtained by calculating the sensitivity and specificity of a test at every possible cut‐off point, and plotting sensitivity against 1‐specificity. The curve may be used to select optimal cut‐off values for a test result, to assess the diagnostic accuracy of a test, and to compare the usefulness of different tests.

Integrity signals

Registry

Crossref · 10.1111/j.1651-2227.2006.00178.x

Cites

17 of 17 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Estimation of the Youden Index and its Associated Cutoff Point
Fluss, R., Faraggi, D., & Reiser, B. (2005) · Biometrical Journal · doi:10.1002/bimj.200410135 · Cited by 2,656
Principles and practical application of the receiver-operating characteristic analysis for diagnostic tests
Greiner, M., Pfeiffer, D., & Smith, R. D. (2000) · Preventive Veterinary Medicine · doi:10.1016/s0167-5877(00)00115-x · Cited by 1,923
The Inconsistency of “Optimal” Cutpoints Obtained using Two Criteria based on the Receiver Operating Characteristic Curve
Perkins, N. J., & Schisterman, E. F. (2006) · American Journal of Epidemiology · doi:10.1093/aje/kwj063 · Cited by 1,794
A readers' guide to the interpretation of diagnostic test properties: clinical example of sepsis
Fischer, J. E., Bachmann, L. M., & Jaeschke, R. (2003) · Intensive Care Medicine · doi:10.1007/s00134-003-1761-8 · Cited by 890
Understanding diagnostic tests 1: sensitivity, specificity and predictive values
Akobeng, A. K. (2007) · Acta Paediatrica · doi:10.1111/j.1651-2227.2006.00180.x · Cited by 711
Self-citation
Statistics Notes: Diagnostic tests 3: receiver operating characteristic plots
Altman, D. G., & Bland, J. M. (1994) · BMJ · doi:10.1136/bmj.309.6948.188 · Cited by 599
ROC Curves in Clinical Chemistry: Uses, Misuses, and Possible Solutions
Obuchowski, N. A., Lieber, M. L., & Wians, F. H. (2004) · Clinical Chemistry · doi:10.1373/clinchem.2004.031823 · Cited by 358
How to report statistics in medicine: annotated guidelines for authors, editors, and reviewers
(1997) · Choice Reviews Online · doi:10.5860/choice.35-1556 · Cited by 309
Diagnostic markers of infection: comparison of procalcitonin with C reactive protein and leucocyte count
Hatherill, M., Tibby, S. M., Sykes, K., Turner, C., & Murdoch, I. (1999) · Archives of Disease in Childhood · doi:10.1136/adc.81.5.417 · Cited by 260
How to Report Statistics in Medicine
Lang, T. A., & Secic, M. (1997) · The Nurse Practitioner · doi:10.1097/00006205-199705000-00022 · Cited by 209

Show all 17 references

Research works printed but not linked: 1

Akobeng AK, Understanding diagnostic tests 2: using likelihood ratios to estimate probability of disease, Acta Paediatr

Sorted by citations

Items 601–700 of 1,001

Screening Tool for Anxiety Disorders: Development and Validation of the Korean Anxiety Screening Assessment
Kim, Y.-S., Park, Y., Cho, G., Park, K., Kim, S.-H., Baik, S. Y., Kim, C. L., Jung, S., Lee, W.-H., Choi, Y., et al. (2018) · Psychiatry Investigation · doi:10.30773/pi.2018.09.27.2 · Cited by 22
Predictors for development of denosumab-induced hypocalcaemia in cancer patients with bone metastases determined by ordered logistic regression analysis
Kanbayashi, Y., Sakaguchi, K., Hongo, F., Ishikawa, T., Tabuchi, Y., Ukimura, O., Takayama, K., & Taguchi, T. (2021) · Scientific Reports · doi:10.1038/s41598-020-80243-y · Cited by 22
Association between mental health and physical activity in patients with chronic heart failure
Izawa, K. P., Watanabe, S., Oka, K., Hiraki, K., Morio, Y., Kasahara, Y., Osada, N., Omiya, K., & Shimizu, H. (2013) · Disability and Rehabilitation · doi:10.3109/09638288.2013.785604 · Cited by 22
Baseline Iron Indices as Predictors of Hemoglobin Improvement in Anemic Vietnamese Women Receiving Weekly Iron-Folic Acid Supplementation and Deworming
Pasricha, S., Casey, G. J., Phuc, T. Q., Mihrshahi, S., MacGregor, L., Montresor, A., Tien, N. T., & Biggs, B. (2009) · American Journal of Tropical Medicine and Hygiene · doi:10.4269/ajtmh.2009.09-0162 · Cited by 22
New Prediction Rule for Mortality in Acute Mesenteric Ischemia
Haga, Y., Odo, M., Homma, M., Komiya, K., Takeda, K., Koike, S., Takahashi, T., Hiraka, K., Yamashita, H., & Tanakaya, K. (2009) · Digestion · doi:10.1159/000219367 · Cited by 22
Leptin and uric acid as predictors of metabolic syndrome in jordanian adults
Obeidat, A. A., Ahmad, M. N., Haddad, F., & Azzeh, F. S. (2016) · Nutrition Research and Practice · doi:10.4162/nrp.2016.10.4.411 · Cited by 22
The Clinical Utility of Informants' Appraisals on Prospective and Retrospective Memory in Patients with Early Alzheimer's Disease
Hsu, Y., Huang, C.-F., Tu, M.-C., & Hua, M. (2014) · PLoS ONE · doi:10.1371/journal.pone.0112210 · Cited by 24
Predictive factors of a beneficial quality of life outcome in patients undergoing primary sinonasal surgery: a population-based prospective cohort study
Alakärppä, A. I., Koskenkorva, T. J., Koivunen, P. T., & Alho, O. (2018) · European Archives of Oto-Rhino-Laryngology · doi:10.1007/s00405-018-4918-3 · Cited by 21
Total Motile Sperm Count in Adolescent Boys with Varicocele is Associated with Hormone Levels and Total Testicular Volume
Batavia, J. V., Lawton, E., Frazier, J. R., Zderic, S. A., Zaontz, M. R., Shukla, A. R., Srinivasan, A. K., Weiß, D., Long, C., Canning, D. A., et al. (2020) · The Journal of Urology · doi:10.1097/ju.0000000000001405 · Cited by 22
Neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio may predict pediatric ovarian torsion: A mono-institutional experience and review of literature.
Nissen, M., Sander, V., Rogge, P., Alrefai, M., & Tröbs, R. (2020) · Journal of Pediatric and Adolescent Gynecology · doi:10.1016/j.jpag.2020.12.003 · Cited by 17
Towards novel osteoarthritis biomarkers: Multi-criteria evaluation of 46,996 segmented knee MRI data from the Osteoarthritis Initiative
Tack, A., Ambellan, F., & Zachow, S. (2021) · PLoS ONE · doi:10.1371/journal.pone.0258855 · Cited by 23
Risk factors for severe infections in secondary immunodeficiency: a retrospective US administrative claims study in patients with hematological malignancies
Jolles, S. R. A., Smith, B. D., Vinh, D. C., Mallick, R., Espinoza, G., DeKoven, M., & Divino, V. M. (2021) · Leukemia & lymphoma/Leukemia and lymphoma · doi:10.1080/10428194.2021.1992761 · Cited by 22
Physical Activity and Sarcopenia in Community-Dwelling Older Adults with Long-Term Care Insurance
Kitamura, M., Izawa, K. P., Ishihara, K., Matsuda, H., Okamura, S., & Fujioka, K. (2021) · European Journal of Investigation in Health Psychology and Education · doi:10.3390/ejihpe11040114 · Cited by 21
Development of modified rapid entire body assessment (MOREBA) method for predicting the risk of musculoskeletal disorders in the workplaces
Yazdanirad, S., Pourtaghi, G., Raei, M., & Ghasemi, M. (2022) · BMC Musculoskeletal Disorders · doi:10.1186/s12891-022-05011-7 · Cited by 21
Sensitivity of dose‐estimations for acute acetaminophen overdose in predicting hepatotoxicity risk using the Rumack‐Matthew Nomogram
Chomchai, S., Mekavuthikul, P., Phuditshinnapatra, J., & Chomchai, C. (2022) · Pharmacology Research & Perspectives · doi:10.1002/prp2.920 · Cited by 21
Machine Learning Algorithms: Prediction and Feature Selection for Clinical Refracture after Surgically Treated Fragility Fracture
Shimizu, H., Enda, K., Shimizu, T., Ishida, Y., Ishizu, H., Ise, K., Tanaka, S., & Iwasaki, N. (2022) · Journal of Clinical Medicine · doi:10.3390/jcm11072021 · Cited by 19
Efficacy of plasma ß-hydroxybutyrate concentration as a marker for diabetes mellitus in acutely sick cats
Zeugswetter, F. K., Handl, S., Iben, C., & Schwendenwein, I. (2010) · Journal of Feline Medicine and Surgery · doi:10.1016/j.jfms.2009.10.002 · Cited by 21
Genome‐wide analysis of DNA methylation changes induced by gestational arsenic exposure in liver tumors
Suzuki, T., Yamashita, S., Ushijima, T., Takumi, S., Sano, T., Michikawa, T., & Nohara, K. (2013) · Cancer Science · doi:10.1111/cas.12298 · Cited by 21
Sperm Penetration Assay as an Indicator of Bull Fertility
Park, Y.-J., Mohamed, E. A., Oh, S., Yoon, S., Kwon, W., Heung-Ruil, K. I. M., Lee, M.-S., Lee, K., & Pang, M. (2012) · Journal of Reproduction and Development · doi:10.1262/jrd.11-067h · Cited by 21
Upper and Lower Extremity Muscle Strength Levels Associated With an Exercise Capacity of 5 Metabolic Equivalents in Male Patients With Heart Failure
Izawa, K. P., Watanabe, S., Oka, K., Hiraki, K., Morio, Y., Kasahara, Y., Watanabe, Y., Katata, H., Osada, N., & Omiya, K. (2012) · Journal of Cardiopulmonary Rehabilitation and Prevention · doi:10.1097/hcr.0b013e31824bd886 · Cited by 20
Association between estimated glomerular filtration rate and peripheral arterial disease
Yamasaki, S., Izawa, A., Koshikawa, M., Saigusa, T., Ebisawa, S., Miura, T., Shiba, Y., Tomita, T., Miyashita, Y., Koyama, J., et al. (2015) · Journal of Cardiology · doi:10.1016/j.jjcc.2015.01.011 · Cited by 21
Predictive validity of the AUDIT for hazardous alcohol consumption in recently released prisoners
Thomas, E. E., Degenhardt, L. J., Alati, R., & Kinner, S. A. (2013) · Drug and Alcohol Dependence · doi:10.1016/j.drugalcdep.2013.10.019 · Cited by 21
Pain Prediction From ECG in Vascular Surgery
Adjei, T., von Rosenberg, W., Goverdovsky, V., Powezka, K., Jaffer, U., & Mandic, D. P. (2017) · IEEE Journal of Translational Engineering in Health and Medicine · doi:10.1109/jtehm.2017.2734647 · Cited by 21
Determining the cut-off value for knee extensor strength for identifying independence in gait in chronic stroke survivors
Akazawa, N., Okawa, N., Tamura, K., & Moriyama, H. (2017) · Journal of Rehabilitation Medicine · doi:10.2340/16501977-2279 · Cited by 21
Shock index increase from the field to the emergency room is associated with higher odds of massive transfusion in trauma patients with stable blood pressure: A cross-sectional analysis
Wu, S., Rau, C., Kuo, S. C. H., Hsu, S.-Y., Hsieh, H.-Y., & Hsieh, C. (2019) · PLoS ONE · doi:10.1371/journal.pone.0216153 · Cited by 21
To randomize, or not to randomize, that is the question: using data from prior clinical trials to guide future designs
Vanderbeek, A. M., Ventz, S., Rahman, R. M., Fell, G. G., Cloughesy, T. F., Wen, P. Y., Trippa, L., & Alexander, B. M. (2019) · Neuro-Oncology · doi:10.1093/neuonc/noz097 · Cited by 21
Cathepsin Z as a novel potential biomarker for osteoporosis
Dera, A. A., Ranganath, L. R., Barraclough, R., Vinjamuri, S., Hamill, S., & Barraclough, D. L. (2019) · Scientific Reports · doi:10.1038/s41598-019-46068-0 · Cited by 21
Comparing the measurement properties and relationship to gait speed recovery of the Mini-Balance Evaluation Systems Test and the Berg Balance Scale in ambulatory individuals with subacute stroke
Miyata, K., Hasegawa, S., Iwamoto, H., Otani, T., Kaizu, Y., Shinohara, T., & Usuda, S. (2020) · Physical Therapy Research · doi:10.1298/ptr.e10004 · Cited by 21
Tumour Cannabinoid CB1 Receptor and Phosphorylated Epidermal Growth Factor Receptor Expression Are Additive Prognostic Markers for Prostate Cancer
Fowler, C. J., Hammarsten, P., & Bergh, A. R. J. (2010) · PLoS ONE · doi:10.1371/journal.pone.0015205 · Cited by 21
Diagnostic Ability of Obesity Measures to Identify Metabolic Risk Factors in South African Women
Evans, J., Micklesfield, L. K., Jennings, C., Levitt, N. S., Lambert, E. V., Olsson, T., & Goedecke, J. H. (2011) · Metabolic Syndrome and Related Disorders · doi:10.1089/met.2011.0034 · Cited by 21
How Much Distress Is Too Much on Deployed Operations? Validation of the Kessler Psychological Distress Scale (K10) for Application in Military Operational Settings
Blanc, S. J.-R., Zamorski, M. A., Ivey, G. W., Edge, H. M., & Hill, K. M. (2014) · Military Psychology · doi:10.1037/mil0000033 · Cited by 22
Using the WHO Self-Reporting Questionnaire-20 (SRQ-20) to Detect Symptoms of Common Mental Disorders among Pregnant Women in Vietnam: a Validation Study
Do, T. T. H., Bui, Q. T. T., Ha, B. T. T., Le, T. M., Le, V. T., Nguyen, Q.-C. T., Lakin, K. J., Dang, T. T., Bui, L. V., Le, T. C., et al. (2023) · International Journal of Women's Health · doi:10.2147/ijwh.s404993 · Cited by 20
Classification of nomophobia among Chinese college students: Evidence from latent profile and ROC analysis
Luo, J., Bei, D.-L., Gong, J., & Wang, M. (2024) · Journal of Behavioral Addictions · doi:10.1556/2006.2024.00013 · Cited by 22
Assessment of Family Functioning and Eating Disorders – The Mediating Role of Self-Esteem
Kroplewski, Z., Szcześniak, M., Furmańska, J., & Gójska, A. (2019) · Frontiers in Psychology · doi:10.3389/fpsyg.2019.00921 · Cited by 22
Patient’s age and d-dimer levels predict the prognosis in patients with TAFRO syndrome
Kawabata, H., Fujimoto, S., Sakai, T., Yanagisawa, H., Kitawaki, T., Nara, K., Hagihara, M., Yamamoto, H., Tanimizu, M., Kato, C., et al. (2021) · International Journal of Hematology · doi:10.1007/s12185-021-03159-x · Cited by 20
Relationships Between WUT (Body Weight, Urine Color, and Thirst Level) Criteria and Urine Indices of Hydration Status
Sekiguchi, Y., Benjamin, C. L., Butler, C. R., Morrissey, M. C., Filep, E. M., Stearns, R. L., Lee, E. C., & Casa, D. J. (2021) · Sports Health A Multidisciplinary Approach · doi:10.1177/19417381211038494 · Cited by 20
Determining a cutoff score of caregiver burden for predicting depression among family caregivers in a large population‐based sample
Arai, Y., & Zarit, S. H. (2014) · International Journal of Geriatric Psychiatry · doi:10.1002/gps.4174 · Cited by 20
Utility of Contrast-enhanced Ultrasonography with Perflubutane for Determining Histologic Grade in Hepatocellular Carcinoma
Tada, T., Kumada, T., Toyoda, H., Ito, T., Sone, Y., Kaneoka, Y., Maeda, A., Okuda, S., Otobe, K., & Takahashi, K. P. (2015) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2015.07.023 · Cited by 20
Fasting capillary blood glucose: an appropriate measurement in screening for diabetes and pre-diabetes in low-resource rural settings.
Zhao, X., Zhao, W., Zhang, H., Li, J., Shu, Y., Li, S., Cai, L., Zhou, J., Li, Y., & Hu, R. (2013) · PubMed · doi:10.3275/8304 · Cited by 20
Validation of the Essener Questionnaire of Age and Sleepiness in the Elderly Using Pupillometry
Frohnhofen, H., Fulda, S., Frohnhofen, K., & Popp, R. (2012) · Advances in experimental medicine and biology · doi:10.1007/978-94-007-4546-9_17 · Cited by 20
Adverse Effects of Excessive Leucine Intake Depend on Dietary Protein Intake: A Transcriptomic Analysis to Identify Useful Biomarkers
Imamura, W., Yoshimura, R., Takai, M., Yamamura, J., Kanamoto, R., & Kato, H. (2013) · Journal of Nutritional Science and Vitaminology · doi:10.3177/jnsv.59.45 · Cited by 20
Urinary levels of TGF β-1 and of cytokines in patients with prenatally detected nephrouropathies
Vasconcelos, M. A., Bouzada, M. C. F., da Silveira, K. D., de Moura, L. R., Santos, F. F., de Oliveira, J. M. G., Carvalho, F. F., Teixeira, M. M., Silva, A. C. S. E., & OLIVEIRA, E. A. (2011) · Pediatric Nephrology · doi:10.1007/s00467-011-1802-4 · Cited by 20
Differences in daily in-hospital physical activity and geriatric nutritional risk index in older cardiac inpatients: preliminary results
Izawa, K. P., Watanabe, S., Oka, K., Osada, N., Omiya, K., Brubaker, P. H., & Shimizu, H. (2014) · Aging Clinical and Experimental Research · doi:10.1007/s40520-014-0233-z · Cited by 20
Ability of walking without a walking device in patients with spinal cord injury as determined using data from functional tests
Poncumhak, P., Saengsuwan, J., & Amatachaya, S. (2013) · Journal of Spinal Cord Medicine · doi:10.1179/2045772313y.0000000160 · Cited by 21
A preliminary study of movement intensity during a Go/No-Go task and its association with ADHD outcomes and symptom severity
Li, F., Zheng, Y., Smith, S. D., Shic, F., Moore, C. C., Zheng, X., Qi, Y., Liu, Z., & Leckman, J. F. (2016) · Child and Adolescent Psychiatry and Mental Health · doi:10.1186/s13034-016-0135-2 · Cited by 20
The Optimal Cutoff Point for Expressing Revised Edmonton Symptom Assessment System Scores as Binary Data Indicating the Presence or Absence of Symptoms
Kako, J., Kobayashi, M., Kanno, Y., Ogawa, A., Miura, T., & Matsumoto, Y. (2018) · American Journal of Hospice and Palliative Medicine® · doi:10.1177/1049909118775660 · Cited by 20
The Influence of Human Demography on Land Cover Change in the Great Lakes States, USA
Ducey, M. J., Johnson, K. M., Belair, E. P., & Cook, B. D. (2018) · Environmental Management · doi:10.1007/s00267-018-1102-x · Cited by 20
Comparison of the Apnea-Hypopnea Index Determined by a Peripheral Arterial Tonometry-Based Device With That Determined by Polysomnography ― Results From a Multicenter Study ―
Kasai, T., Takata, Y., Yoshihisa, A., Takeishi, Y., Chin, K., Ando, S., Kawana, F., & Momomura, S. (2020) · Circulation Reports · doi:10.1253/circrep.cr-20-0097 · Cited by 20
Fibrinogen/albumin ratio index is an independent predictor of recurrence-free survival in patients with intrahepatic cholangiocarcinoma following surgical resection
Liu, H., Qiu, G., Hu, F., & Wu, H. M. (2021) · World Journal of Surgical Oncology · doi:10.1186/s12957-021-02330-2 · Cited by 20
Decorin in human oral cancer: A promising predictive biomarker of S-1 neoadjuvant chemosensitivity
Kasamatsu, A., Uzawa, K., Minakawa, Y., Ishige, S., Kasama, H., Endo‐Sakamoto, Y., Ogawara, K., Shiiba, M., Takiguchi, Y., & Tanzawa, H. (2014) · Biochemical and Biophysical Research Communications · doi:10.1016/j.bbrc.2014.12.093 · Cited by 20
Arterial pressure variations as parameters of brain perfusion in response to central blood volume depletion and repletion
Bronzwaer, A.-S. G. T., Stok, W. J., Westerhof, B. E., & LIESHOUT, J. J. V. (2014) · Frontiers in Physiology · doi:10.3389/fphys.2014.00157 · Cited by 20
Reliability and discriminative ability of the spinal cord independence measure III (Thai version)
Wannapakhe, J., Saensook, W., Keawjoho, C., & Amatachaya, S. (2015) · Spinal Cord · doi:10.1038/sc.2015.114 · Cited by 20
Immunohistochemical analysis of IgA expression differentiates IgG4-related disease from plasma cell-type Castleman disease
Manabe, A., Igawa, T., Takeuchi, M., Gion, Y., Yoshino, T., & Sato, Y. (2016) · Medical Molecular Morphology · doi:10.1007/s00795-016-0145-4 · Cited by 20
Diagnostic and prognostic value of bladder cancer-related transcript markers in urine
Schmidt, J., Propping, C., Siow, W.-Y., Lohse-Fischer, A., Toma, M. I., Baldauf-Twelker, A., Hakenberg, O. W., Wirth, M. P., & Fuessel, S. (2015) · Journal of Cancer Research and Clinical Oncology · doi:10.1007/s00432-015-2037-8 · Cited by 19
Thyroglobulin levels before radioactive iodine therapy and dynamic risk stratification after 1 year in patients with differentiated thyroid cancer
Bandeira, L., Padovani, R. D. P., Ticly, A. L. T., Cury, A. N., Scalissi, N. M., Marone, M. M. S., & Ferraz, C. (2017) · Archives of Endocrinology and Metabolism · doi:10.1590/2359-3997000000308 · Cited by 19
Psychological Status as an Effect Modifier of the Association Between Sinonasal Instrument and Imaging Results
Zhou, A. S., Prince, A. A., Maxfield, A. Z., & Shin, J. J. (2020) · Otolaryngology · doi:10.1177/0194599820926129 · Cited by 19
Novel insights into the biopharmaceutical potential, comparative phytochemical analysis and multivariate analysis of different extracts of shea butter tree -Vitellaria paradoxa C. F. Gaertn
Sinan, K. I., Martinović, L. S., Peršurić, Ž., Pavelić, S. K., Etienne, O. K., Mahomoodally, M. F., Sadeer, N. B., & Zengin, G. (2020) · Process Biochemistry · doi:10.1016/j.procbio.2020.07.028 · Cited by 20
Objective Analysis of Movement in Subjects with ADHD. Multidisciplinary Control Tool for Students in the Classroom
Tortosa, M. L. S., Carrasco, F. F., Mora-Lizán, F., & Rizo-Maestre, C. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17155620 · Cited by 19
Higher Preimplantation Opioid Doses Associated With Long-Term Spinal Cord Stimulation Failure in 211 Patients With Failed Back Surgery Syndrome
Nissen, M., Ikäheimo, T.-M., Huttunen, J., Leinonen, V., Jyrkkänen, H.-K., & zu Fraunberg, M. V. U. (2020) · Neuromodulation Technology at the Neural Interface · doi:10.1111/ner.13297 · Cited by 19
Comprehensive analysis of key m5C modification-related genes in type 2 diabetes
Song, Y., Jiang, Y. Y., Shi, L., He, C., Zhang, W., Xu, Z., Yang, M., & Xu, Y. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.1015879 · Cited by 19
Predictive Outcomes of Deep Learning Measurement of the Anterior Glottic Angle in Bilateral Vocal Fold Immobility
DeVore, E. K., Adamian, N., Jowett, N., Wang, T. V., Song, P. C., Franco, R. A., & Naunheim, M. R. (2022) · The Laryngoscope · doi:10.1002/lary.30473 · Cited by 19
Diagnostic accuracy of the five times stand-to-sit test for the screening of global muscle weakness in community-dwelling older women
Porto, J. M., Peres-Ueno, M. J., Braghin, R. D. M. B., Scudilio, G. M., & de Abreu, D. C. C. (2022) · Experimental Gerontology · doi:10.1016/j.exger.2022.112027 · Cited by 19
Association between swallowing muscle mass and dysphagia in older adults: A case–control study
Umay, E., Akaltun, M. S., & Uz, C. (2023) · Journal of Oral Rehabilitation · doi:10.1111/joor.13439 · Cited by 20
Indicators and outcome of liver transplantation in acute liver decompensation after flares of hepatitis B
Lee, W., Chou, H., Wu, T.-J., Lee, C.-S., Lee, C.-F., & Chan, K. (2010) · Journal of Viral Hepatitis · doi:10.1111/j.1365-2893.2010.01295.x · Cited by 19
Grey scale texture analysis of endobronchial ultrasound mini probe images for prediction of benign or malignant aetiology
Nguyen, P., Bashirzadeh, F., Hundloe, J. T., Salvado, O., Dowson, N., Ware, R. S., Masters, I. B., Kumar, A. S. R., & Fielding, D. I. (2015) · Respirology · doi:10.1111/resp.12577 · Cited by 19
Differences in physical performance based on the Geriatric Nutritional Risk Index in elderly female cardiac patients
Izawa, K. P., Watanabe, S., Oka, K., Mogamiya, T., Tada, M., Nakata, S., Nitobe, S., Yoshizawa, K., Hirano, Y., Osada, N., et al. (2014) · Aging Clinical and Experimental Research · doi:10.1007/s40520-014-0264-5 · Cited by 19
Predicting distributions of seven bitterling fishes in northern Kyushu, Japan
Onikura, N., Nakajima, J., Miyake, T., Kawamura, K., & Fukuda, S. (2011) · Ichthyological Research · doi:10.1007/s10228-011-0260-0 · Cited by 19
Protein Profiling in Hepatocellular Carcinoma by Label-Free Quantitative Proteomics in Two West African Populations
Fye, H. K. S., Wright, C., Kramer, H., Camey, S. A., da Costa, A. N., Jeng, A., Bah, A., Kirk, G. D., Sharif, M. I. F., Ladep, N. G., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0068381 · Cited by 19
The German version of the Painful and Provocative Events Scale: A psychometric investigation
Teismann, T., Forkmann, T., Wachtel, S., Edel, M., Nyhuis, P. W., & Glaesmer, H. (2015) · Psychiatry Research · doi:10.1016/j.psychres.2014.12.060 · Cited by 19
Coupling electrochemical response of a DNA biosensor with PCR for Neisseria gonorrhoeae detection
Verma, R., Sood, S., Singh, R., Sumana, G., Bala, M., Sharma, V., Samantaray, J. C., Pandey, R. M., & Malhotra, B. D. (2013) · Diagnostic Microbiology and Infectious Disease · doi:10.1016/j.diagmicrobio.2013.09.010 · Cited by 20
A New Quantitative Index for Baseline Renal Transplant Scintigraphy With 99mTc-DTPA in Evaluation of Delayed Graft Function and Prediction of 1-Year Graft Function
Yazıcı, B., Oral, A., Gökalp, C., Akgün, A., Töz, H., & Hoşçoşkun, C. (2015) · Clinical Nuclear Medicine · doi:10.1097/rlu.0000000000001020 · Cited by 19
Predicting arterial blood gas and lactate from central venous blood analysis in critically ill patients: a multicentre, prospective, diagnostic accuracy study
Boulain, T., Garot, D., Vignon, P., Lascarrou, J.-B., Benzékri-Lefèvre, D., & Dequin, P. (2016) · British Journal of Anaesthesia · doi:10.1093/bja/aew261 · Cited by 20
Haptoglobin is a serological biomarker for adenocarcinoma lung cancer by using the ProteomeLab PF2D combined with mass spectrometry.
Chang, Y.-K., Lai, Y., Chu, Y., Lee, M., Huang, C., & Wu, S. (2016) · Europe PMC (PubMed Central) · Cited by 19
Lipreading Ability and Its Cognitive Correlates in Typically Developing Children and Children With Specific Language Impairment
Heikkilä, J., Lonka, E., Ahola, S., Meronen, A., & Tiippana, K. M. (2017) · Journal of Speech Language and Hearing Research · doi:10.1044/2016_jslhr-s-15-0071 · Cited by 19
Cerebrospinal fluid procalcitonin as a biomarker of bacterial meningitis in neonates
Reshi, Z., Nazir, M., Wani, W. A., Malik, M. A., Iqbal, J., & Wajid, S. (2017) · Journal of Perinatology · doi:10.1038/jp.2017.73 · Cited by 19
Accuracy of CT chest without oral contrast for ruling out esophageal perforation using fluoroscopic esophagography as reference standard: a retrospective study
Awais, M., Qamar, S., Rehman, A., Baloch, N. U.-A., & Shafqat, G. (2018) · European Journal of Trauma and Emergency Surgery · doi:10.1007/s00068-018-0929-4 · Cited by 19
Low levels of tumour suppressor miR-655 in plasma contribute to lymphatic progression and poor outcomes in oesophageal squamous cell carcinoma
Kiuchi, J., Komatsu, S., Imamura, T., Nishibeppu, K., Shoda, K., Arita, T., Kosuga, T., Konishi, H., Shiozaki, A., Okamoto, K. K., et al. (2019) · Molecular Cancer · doi:10.1186/s12943-018-0929-3 · Cited by 19
Incorporation of shear wave elastography into a prediction model in the assessment of cervical lymph nodes
Lo, W., Hsu, W., Wang, C., Cheng, P., & Liao, L. (2019) · PLoS ONE · doi:10.1371/journal.pone.0221062 · Cited by 19
Inverse correlation between Interleukin-34 and gastric cancer, a potential biomarker for prognosis
Liu, Q., Zhang, Y., Zhang, J., Tao, K., Hambly, B. D., & Bao, S. (2020) · Cell & Bioscience · doi:10.1186/s13578-020-00454-8 · Cited by 19
Prognostic factors and clinical nomogram predicting survival in high-grade glioma
Tunthanathip, T., Ratanalert, S., Sae-heng, S., Oearsakul, T., Sakarunchai, I., Kaewborisutsakul, A., Chotsampancharoen, T., Intusoma, U., Kitkhuandee, A., & Vaniyapong, T. (2020) · Journal of Cancer Research and Therapeutics · doi:10.4103/jcrt.jcrt_233_19 · Cited by 19
Claudicating patients with peripheral artery disease have meaningful improvement in walking speed after supervised exercise therapy
Rahman, H., Pipinos, I. I., Johanning, J. M., Casale, G. P., Williams, M. A., Thompson, J. R., O'Neill-Castro, Y., & Myers, S. A. (2021) · Journal of Vascular Surgery · doi:10.1016/j.jvs.2021.04.069 · Cited by 19
Usefulness of combining gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid-enhanced magnetic resonance imaging and contrast-enhanced ultrasound for diagnosing the macroscopic classification of small hepatocellular carcinoma
Kobayashi, T., Aikata, H., Hatooka, M., Morio, K., Morio, R., Kan, H., Fujino, H., Fukuhara, T., Masaki, K., Ohno, A., et al. (2015) · European Radiology · doi:10.1007/s00330-015-3725-0 · Cited by 19
Puntos de corte del perímetro de la cintura para identificar sujetos con resistencia a la insulina en una población colombiana
Gallo‐Villegas, J., Múnera, J. E. O., Arias, J. K. B., & Aristizábal-Ocampo, D. (2013) · Acta Médica Colombiana · doi:10.36104/amc.2013.73 · Cited by 19
Distinguishing adrenal adenomas from non-adenomas on dynamic enhanced CT: A comparison of 5 and 10 min delays after intravenous contrast medium injection
Kumagae, Y., Fukukura, Y., Takumi, K., Shindo, T., Tateyama, A., Kamiyama, T., Kamimura, K., & Nakajo, M. (2013) · Clinical Radiology · doi:10.1016/j.crad.2013.01.016 · Cited by 19
Exome sequencing reveals a nonsense mutation in MMP13 as a new cause of autosomal recessive metaphyseal anadysplasia
Li, D., Weber, D. R., Deardorff, M. A., Hakonarson, H. H., & Levine, M. A. (2014) · European Journal of Human Genetics · doi:10.1038/ejhg.2014.76 · Cited by 19
A-waves in Guillain–Barré syndrome: Correlation with electrophysiological subtypes and antiganglioside antibodies
Kawakami, S., Sonoo, M., Kadoya, A., Chiba, A., & Shimizu, T. (2011) · Clinical Neurophysiology · doi:10.1016/j.clinph.2011.10.005 · Cited by 19
Scoring the home falls and accidents screening tool for health professionals ( HOME FAST ‐ HP ): Evidence from one epidemiological study
Mackenzie, L. A., & Byles, J. (2018) · Australian Occupational Therapy Journal · doi:10.1111/1440-1630.12467 · Cited by 19
The Use of Multi-parametric Biomarker Profiles May Increase the Accuracy of ASD Prediction
Hassan, W. M., AL-Ayadhi, L. Y., Bjørklund, G., Alabdali, A. N., Chirumbolo, S., & El-Ansary, A. K. (2018) · Journal of Molecular Neuroscience · doi:10.1007/s12031-018-1136-9 · Cited by 19
Differential Diagnosis of Reactive Mesothelial Cells and Malignant Mesothelioma Cells Using the Cell Proliferation Markers Minichromosome Maintenance Protein 7, Geminin, Topoisomerase II Alpha and Ki-67
Kimura, F., Okayasu, I., Kakinuma, H., Satoh, Y., Kuwao, S., Saegusa, M., & Watanabe, J. (2013) · Acta Cytologica · doi:10.1159/000350262 · Cited by 18
Optimal body mass index cut‐off points for prediction of incident diabetes in a Chinese population
Ma, H., Wu, X., Guo, X., Yang, J., Ma, X., Lv, M., & Li, Y. (2018) · Journal of Diabetes · doi:10.1111/1753-0407.12785 · Cited by 19
Ratio of IL-8 in CSF Versus Serum Is Elevated in Patients with Unruptured Brain Aneurysm
Kamińska, J., Łysoń, T., Chrzanowski, R., Sawicki, K., Milewska, A. J., Tylicka, M., Dorf, J., Matowicka-Karna, J., Dymicka-Piekarska, V., Mariak, Z. D., et al. (2020) · Journal of Clinical Medicine · doi:10.3390/jcm9061761 · Cited by 18
Hyperspectral Imaging Combined with Machine Learning for the Detection of Fusiform Rust Disease Incidence in Loblolly Pine Seedlings
Pandey, P., Payn, K. G., Lu, Y., Heine, A. J., Walker, T., Acosta, J. J., & Young, S. N. (2021) · Remote Sensing · doi:10.3390/rs13183595 · Cited by 19
PHOX2B is a Sensitive and Specific Marker for the Histopathological Diagnosis of Pheochromocytoma and Paraganglioma
Miyauchi, M., Akashi, T., Furukawa, A., Uchida, K., Tamura, T., Ando, N., Kirimura, S., Shintaku, H., Yamamoto, K., Ito, T., et al. (2022) · Endocrine Pathology · doi:10.1007/s12022-022-09730-4 · Cited by 18
Finger Tapping Test for Assessing the Risk of Mild Cognitive Impairment
Suzumura, S., Osawa, A., Kanada, Y., Maeda, K., Takano, E., Sugioka, J., Maeda, N., Nagahama, T., Shiramoto, K., Kuno, K., et al. (2022) · Hong Kong Journal of Occupational Therapy · doi:10.1177/15691861221109872 · Cited by 18
CNN-assisted accurate smartphone testing of μPAD for pork sausage freshness
Liu, Y., Zhang, Y., Long, F., Bai, J., Huang, Y., & Gao, H. X. (2023) · Journal of Food Engineering · doi:10.1016/j.jfoodeng.2023.111772 · Cited by 18
Risk prediction model of clinical mastitis in lactating dairy cows based on machine learning algorithms
Luo, W., Dong, Q., & Feng, Y. (2023) · Preventive Veterinary Medicine · doi:10.1016/j.prevetmed.2023.106059 · Cited by 21
Pain Identifies Squamous Cell Carcinoma in Organ Transplant Recipients: The SCOPE-ITSCC PAIN Study
Bavinck, J. N. B., Harwood, C. A., Genders, R. E., Wisgerhof, H. C., Plasmeijer, E. I., Mitchell, L., Olasz, E. B., Mosel, D. D., Pokorney, M. S., Serra, A. L., et al. (2014) · American Journal of Transplantation · doi:10.1111/ajt.12587 · Cited by 18
Efficacy of transient elastography in screening for large esophageal varices in patients with suspicious or proven liver cirrhosis
Wang, H.-M., Lo, G., Chen, W., Hsu, P., Yu, H., Lin, C., Chan, H., Tsai, W., Tsay, F., Liu, T. J., et al. (2012) · Journal of Digestive Diseases · doi:10.1111/j.1751-2980.2012.00603.x · Cited by 18
Changes in rat urinary porphyrin profiles predict the magnitude of the neurotoxic effects induced by a mixture of lead, arsenic and manganese
Andrade, V., Mateus, M. L., Batoréu, M. C. C., Aschner, M., & dos Santos, A. P. M. (2014) · NeuroToxicology · doi:10.1016/j.neuro.2014.10.009 · Cited by 18
Soluble Isoform of the Receptor for Advanced Glycation End Products as a Biomarker for Postoperative Respiratory Failure after Cardiac Surgery
Uchida, T., Ohno, N., Asahara, M., Yamada, Y., Yamaguchi, O., Tomita, M., & Makita, K. (2013) · PLoS ONE · doi:10.1371/journal.pone.0070200 · Cited by 18

Authors: Akobeng, A. K.
Corresponding author: Akobeng, Anthony K
ORCID: Akobeng, Anthony K
Venue: Acta Paediatrica
Publisher: Wiley
Published: 2007-03-21
Volume: 96
Issue: 5
Pages: 644-647
ISSN: 0365-1436 (ISSN-L)
0803-5253
1651-2227
DOI: 10.1111/j.1651-2227.2006.00178.x
OpenAlex: W2073887351
Cited by: 1,882
References: 17
Citations by year: 2026: 53 · 2025: 95 · 2024: 135 · 2023: 138 · 2022: 167 · 2021: 156 · 2020: 131 · 2019: 116 · 2018: 102 · 2017: 120 · 2016: 113 · 2015: 117 · 2014: 117 · 2013: 98 · 2012: 76
FWCI: 29.05
Type: journal article
Language: en
Open access: closed
Licence: onlinelibrary.wiley.com/termsAndConditions#vor (vor)
Locations: Acta Paediatrica (publishedVersion)
PubMed (publishedVersion)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
Health Sciences › Health Professions › Medical Laboratory Technology › Clinical Laboratory Practices and Quality Control
Keywords: receiver operating characteristic, diagnostic accuracy, cut-point selection, sensitivity, specificity, Youden index, area under the ROC curve, diagnostic test comparison
MeSH terms: C-Reactive Protein, Calcitonin, Humans, Leukocyte Count, Protein Precursors, ROC Curve, Sensitivity and Specificity, Shock, Septic

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Understanding diagnostic tests 3: receiver operating characteristic curves

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks