Logistic Regression: A Brief Primer
Abstract
Academic Emergency Medicine 2011; 18:1099–1104 © 2011 by the Society for Academic Emergency Medicine Regression techniques are versatile in their application to medical research because they can measure associations, predict outcomes, and control for confounding variable effects. As one such technique, logistic regression is an efficient and powerful way to analyze the effect of a group of independent variables on a binary outcome by quantifying each independent variable’s unique contribution. Using components of linear regression reflected in the logit scale, logistic regression iteratively identifies the strongest linear combination of variables with the greatest probability of detecting the observed outcome. Important considerations when conducting logistic regression include selecting independent variables, ensuring that relevant assumptions are met, and choosing an appropriate model building strategy. For independent variable selection, one should be guided by such factors as accepted theory, previous empirical investigations, clinical considerations, and univariate statistical analyses, with acknowledgement of potential confounding variables that should be accounted for. Basic assumptions that must be met for logistic regression include independence of errors, linearity in the logit for continuous variables, absence of multicollinearity, and lack of strongly influential outliers. Additionally, there should be an adequate number of events per independent variable to avoid an overfit model, with commonly recommended minimum “rules of thumb” ranging from 10 to 20 events per covariate. Regarding model building strategies, the three general types are direct/standard, sequential/hierarchical, and stepwise/statistical, with each having a different emphasis and purpose. Before reaching definitive conclusions from the results of any of these methods, one should formally quantify the model’s internal validity (i.e., replicability within the same data set) and external validity (i.e., generalizability beyond the current sample). The resulting logistic regression model’s overall fit to the sample data is assessed using various goodness‐of‐fit measures, with better fit characterized by a smaller difference between observed and model‐predicted values. Use of diagnostic statistics is also recommended to further assess the adequacy of the model. Finally, results for independent variables are typically reported as odds ratios (ORs) with 95% confidence intervals (CIs).
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
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?
Final model diagnostics included checking for independence of errors, absence of multicollinearity, and lack of strongly influential outliers.
“Final model diagnostics included checking for independence of errors, absence of multicollinearity, and lack of strongly influential outliers [53, 55, 56].”
From An enhanced individual placement and support (iPS) intervention based on the model of human occupation (MOHO); a prospective cohort study (2020) · cites this paper as [53]
In general, models with fewer variables are simpler; and without evidence of a significant difference, the simpler model is preferred.
“In general, models with fewer variables are simpler; and without evidence of a significant difference, the simpler model is preferred [53, 54].”
From An enhanced individual placement and support (iPS) intervention based on the model of human occupation (MOHO); a prospective cohort study (2020) · cites this paper as [53]
It is recommended that variable selection should be guided by well-established theory, clinical observations, and preliminary analysis.
“It is recommended that variable selection should be guided by well-established theory, clinical observations, and preliminary analysis [53].”
From An enhanced individual placement and support (iPS) intervention based on the model of human occupation (MOHO); a prospective cohort study (2020) · cites this paper as [53]
Cites
Tabachnick, B. G., & Fidell, L. S. (1983) · Cited by 79,888 · see citing works
An Introduction to the Bootstrap
Efron, B., & Tibshirani, R. J. (1994) · doi:10.1201/9780429246593 · Cited by 40,500 · see citing works
(1990) · Choice Reviews Online · doi:10.5860/choice.27-3338 · Cited by 35,305 · see citing works
An Introduction to the Bootstrap
Efron, B., & Tibshirani, R. J. (1993) · doi:10.1007/978-1-4899-4541-9 · Cited by 21,230 · see citing works
Hosmer, D. W., & Lemeshow, S. (2000) · doi:10.1002/0471722146 · Cited by 20,302 · see citing works
(1963) · Medical Entomology and Zoology · Cited by 15,085 · see citing works
Using Multivariate Statistics (5th Edition)
Tabachnick, B. G., & Fidell, L. S. (2006) · Allyn & Bacon, Inc. eBooks · Cited by 11,189 · see citing works
A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection
Kohavi, R. (1995) · Zenodo · doi:10.5281/zenodo.19712698 · Cited by 10,909 · see citing works
(2007) · Cited by 10,092 · see citing works
A simulation study of the number of events per variable in logistic regression analysis
Peduzzi, P., Concato, J., Kemper, E., Holford, T. R., & Feinstein, A. R. (1996) · Journal of Clinical Epidemiology · doi:10.1016/s0895-4356(96)00236-3 · Cited by 9,655 · see citing works
An Introduction to Categorical Data Analysis
Agresti, A. (2006) · Wiley series in probability and statistics · doi:10.1002/0470114754 · Cited by 6,697 · see citing works
Proceedings of the 16th international joint conference on Artificial Intelligence - IJCAI '99
Colton, S., Bundy, A., & Walsh, T. (1999) · Cited by 2,500 · see citing works
Logistic Regression Using the SAS System : Theory and Application
Allison, P. D. (1999) · Cited by 2,087 · see citing works
Categorical Data Analysis Using the SAS System
Ziegel, E. R., Stokes, M., Davis, C., & Koch, G. (1996) · Technometrics · doi:10.1080/00401706.1996.10484575 · Cited by 2,034 · see citing works
Receiver-operating characteristic analysis for evaluating diagnostic tests and predictive models
Zou, K. H., O’Malley, A. J., & Mauri, L. (2007) · Circulation · doi:10.1161/circulationaha.105.594929 · Cited by 1,473 · see citing works
Darlington, R. B. (1990) · Cited by 986 · see citing works
Categorical Data Analysis Using The SAS1 System
(2001) · Technometrics · doi:10.1198/tech.2001.s617 · Cited by 649 · see citing works
Validation techniques for logistic regression models
Miller, M. E., Hui, S. L., & Tierney, W. M. (1991) · Statistics in Medicine · doi:10.1002/sim.4780100805 · Cited by 322 · see citing works
Advanced Statistics: Missing Data in Clinical Research--Part 2: Multiple Imputation
Newgard, C. D., & Haukoos, J. S. (2007) · Academic Emergency Medicine · doi:10.1197/j.aem.2006.11.038 · Cited by 261 · see citing works
Multivariable Analysis: An Introduction
Feinstein, A. R. (1996) · Medical Entomology and Zoology · Cited by 200 · see citing works
Newgard, C. D., Hedges, J. R., Arthur, M., & Mullins, R. J. (2004) · Academic Emergency Medicine · doi:10.1197/j.aem.2004.02.530 · Cited by 179 · see citing works
Global goodness‐of‐fit tests in logistic regression with sparse data
Kuß, O. (2002) · Statistics in Medicine · doi:10.1002/sim.1421 · Cited by 79 · see citing works
Advanced Statistics: Missing Data in Clinical Research—Part 2: Multiple Imputation
Newgard, C. D., & Haukoos, J. S. (2007) · Academic Emergency Medicine · doi:10.1111/j.1553-2712.2007.tb01856.x · Cited by 67 · see citing works
Hosmer (1997) · doi:10.1002/(sici)1097-0258(19970515)16:9
Flagged first, then by citations
Cited by
Sorted by citations
Association Between Gender-Affirming Surgeries and Mental Health Outcomes
Almazan, A. N., & Keuroghlian, A. S. (2021) · JAMA Surgery · doi:10.1001/jamasurg.2021.0952 · Cited by 395 · see citing works
Texture Analysis of Imaging: What Radiologists Need to Know
Varghese, B. A., Cen, S., Hwang, D., & Duddalwar, V. A. (2019) · American Journal of Roentgenology · doi:10.2214/ajr.18.20624 · Cited by 245 · see citing works
Danopoulos, E., Twiddy, M., West, R., & Rotchell, J. M. (2022) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2021.127861 · Cited by 230 · see citing works
Applications of machine learning in metabolomics: Disease modeling and classification
Galal, A. A., Talal, M., & Moustafa, A. A. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.1017340 · Cited by 190 · see citing works
Assessment of COVID-19 Information Overload Among the General Public
Mohammed, M., Sha’aban, A., Jatau, A. I., Yunusa, I. U., Isa, A., Wada, A. S., Obamiro, K. O., Zainal, H., & Ibrahim, B. (2021) · Journal of Racial and Ethnic Health Disparities · doi:10.1007/s40615-020-00942-0 · Cited by 152 · see citing works
Predicting uptake of aquaculture technologies among smallholder fish farmers in Kenya
Obiero, K. O., Waidbacher, H., Nyawanda, B. O., Munguti, J. M., Manyala, J. O., & Kaunda‐Arara, B. (2019) · Aquaculture International · doi:10.1007/s10499-019-00423-0 · Cited by 142 · see citing works
Liu, X.-B., Fan, K., Huang, X., Ge, J., Liu, Y., & Kang, H. (2024) · Chemical Engineering Journal · doi:10.1016/j.cej.2024.151625 · Cited by 132 · see citing works
Jung, H., Jeon, J., Choi, D., & Park, J.-Y. (2021) · Sustainability · doi:10.3390/su13084120 · Cited by 129 · see citing works
A Systematic Review of Wearable Sensor-Based Technologies for Fall Risk Assessment in Older Adults
Chen, M., Wang, H., Yu, L., Yeung, E., Luo, J., Tsui, K., & Zhao, Y. (2022) · Sensors · doi:10.3390/s22186752 · Cited by 129 · see citing works
Biosensors, Artificial Intelligence Biosensors, False Results and Novel Future Perspectives
Goumas, G. S., Vlachothanasi, E. N., Fradelos, Ε. C., & Mouliou, D. S. (2025) · Diagnostics · doi:10.3390/diagnostics15081037 · Cited by 129 · see citing works
Zhang, X., Kim, J. M., Patzer, R. E., Pitts, S. R., Patzer, A., & Schrager, J. D. (2017) · Methods of Information in Medicine · doi:10.3414/me17-01-0024 · Cited by 128 · see citing works
Use of Health Care Claims Data to Study Patients with Ophthalmologic Conditions
Stein, J. D., Lum, F., Lee, P. P., Rich, W. L., & Coleman, A. L. (2014) · Ophthalmology · doi:10.1016/j.ophtha.2013.11.038 · Cited by 123 · see citing works
Modified 30-second Sit to Stand test predicts falls in a cohort of institutionalized older veterans
Applebaum, E. V., Breton, D., Feng, Z. W., Ta, A.-T., Walsh, K., Chassé, K., & Robbins, S. M. K. (2017) · PLoS ONE · doi:10.1371/journal.pone.0176946 · Cited by 122 · see citing works
Machine Learning Models for Predicting Neonatal Mortality: A Systematic Review
Mangold, C., Zoretic, S., Thallapureddy, K., Moreira, A., Chorath, K. T., & Moreira, A. G. (2021) · Neonatology · doi:10.1159/000516891 · Cited by 121 · see citing works
Nafouanti, M. B., Li, J., Mustapha, N. A., Uwamungu, P., & AL-Alimi, D. (2021) · Applied Geochemistry · doi:10.1016/j.apgeochem.2021.105054 · Cited by 115 · see citing works
Uddin, S., & Lu, H. (2024) · PLoS ONE · doi:10.1371/journal.pone.0301541 · Cited by 117 · see citing works
Diallo, R., Edalo, C., & Awe, O. O. (2024) · STEAM-H: Science, Technology, Engineering, Agriculture, Mathematics & Health · doi:10.1007/978-3-031-72215-8_12 · Cited by 107 · see citing works
Köbe, T., Witte, A. V., Schnelle, A., Tesky, V. A., Pantel, J., Schuchardt, J. P., Hahn, A., Bohlken, J., Grittner, U., & Flöel, A. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00105 · Cited by 107 · see citing works
Two Statistical Approaches to Justify the Use of the Logistic Function in Binary Logistic Regression
Zaidi, A. T., & Luhayb, A. S. M. A. (2023) · Mathematical Problems in Engineering · doi:10.1155/2023/5525675 · Cited by 105 · see citing works
Kiratiratanapruk, K., Temniranrat, P., Sinthupinyo, W., Prempree, P., Chaitavon, K., Porntheeraphat, S., & Prasertsak, A. (2020) · Journal of Sensors · doi:10.1155/2020/7041310 · Cited by 101 · see citing works
Schellekens, D. H. S. M., Hulsewé, K. W. E., van Acker, B. A. C., van Bijnen, A. A. J. H. M., de Jaegere, T. M. H., Sastrowijoto, S. H., Buurman, W. A., & Derikx, J. P. M. (2013) · Academic Emergency Medicine · doi:10.1111/acem.12160 · Cited by 98 · see citing works
Zhang, C., Zhao, Y., Yan, T., Bai, X., Xiao, Q., Gao, P., Li, M., Huang, W., Bao, Y., He, Y., et al. (2020) · Infrared Physics & Technology · doi:10.1016/j.infrared.2020.103550 · Cited by 97 · see citing works
Suhas, S., Manjunatha, N., Kumar, C., Benegal, V., Rao, G. N., Varghese, M., & Gururaj, G. (2023) · Indian Journal of Psychiatry · doi:10.4103/indianjpsychiatry.indianjpsychiatry_827_23 · Cited by 99 · see citing works
Zhou, J., Dong, J., Hou, H., Huang, L., & Li, J. (2024) · Lab on a Chip · doi:10.1039/d3lc01012k · Cited by 92 · see citing works
Mosheva, M., Gross, R., Hertz‐Palmor, N., Hasson‐Ohayon, I., Kaplan, R., Cleper, R., Kreiss, Y., Gothelf, D., & Pessach, I. M. (2021) · Depression and Anxiety · doi:10.1002/da.23140 · Cited by 90 · see citing works
Pettersson, L., Johansson, S., Demmelmaier, I., & Gustavsson, C. (2023) · BMC Public Health · doi:10.1186/s12889-023-15094-z · Cited by 89 · see citing works
Understanding decision curve analysis in clinical prediction model research
Zhao, L., Leng, Y., Hu, Y., Xiao, J., Li, Q., Liu, C., & Mao, Y. (2024) · Postgraduate Medical Journal · doi:10.1093/postmj/qgae027 · Cited by 87 · see citing works
Varghese, B. A., Chen, F., Hwang, D., Palmer, S. L., Abreu, A. L., Ukimura, O., Aron, M., Aron, M., Gill, I. S., Duddalwar, V. A., et al. (2019) · Scientific Reports · doi:10.1038/s41598-018-38381-x · Cited by 86 · see citing works
Kestler, A. M., Buxton, J. A., Meckling, G., Giesler, A., Lee, M., Fuller, K., Quian, H., Marks, D., & Scheuermeyer, F. X. (2016) · Annals of Emergency Medicine · doi:10.1016/j.annemergmed.2016.07.027 · Cited by 83 · see citing works
Mays, S. A., Prowse, T., George, M., & Brickley, M. B. (2018) · American Journal of Physical Anthropology · doi:10.1002/ajpa.23646 · Cited by 82 · see citing works
Cohort study of electronic cigarette use: effectiveness and safety at 24 months
Manzoli, L., Flacco, M. E., Ferrante, M., Vecchia, C. L., Siliquini, R., Ricciardi, W., Marzuillo, C., Villari, P., & Fiore, M. (2016) · Tobacco Control · doi:10.1136/tobaccocontrol-2015-052822 · Cited by 80 · see citing works
Examining empathy and responsiveness in a high-service context
Murray, J. G., Elms, J., & Curran, M. (2019) · International Journal of Retail & Distribution Management · doi:10.1108/ijrdm-01-2019-0016 · Cited by 80 · see citing works
Donisi, L., Cesarelli, G., Coccia, A., Panigazzi, M., Capodaglio, E. M., & D’Addio, G. (2021) · Sensors · doi:10.3390/s21082593 · Cited by 78 · see citing works
Nguyễn, A. T., Tsai, C., Hwang, L., Lai, D., Markham, C. M., & Patel, B. (2016) · PLoS ONE · doi:10.1371/journal.pone.0154599 · Cited by 75 · see citing works
Banerjee, A., & Roy, K. (2024) · Environmental Science Processes & Impacts · doi:10.1039/d4em00173g · Cited by 73 · see citing works
Yan, T., Xu, W., Jiao, L., Duan, L., Gao, P., Zhang, C., & Lv, X. (2021) · Frontiers in Plant Science · doi:10.3389/fpls.2021.604510 · Cited by 71 · see citing works
Burden and Outcomes of Severe Pertussis Infection in Critically Ill Infants*
Straney, L. D., Schibler, A., Ganeshalingham, A., Alexander, J., Festa, M., Slater, A., MacLaren, G., & Schlapbach, L. J. (2016) · Pediatric Critical Care Medicine · doi:10.1097/pcc.0000000000000851 · Cited by 68 · see citing works
Mufti, H. N., Hirsch, G. M., Abidi, S., & Abidi, S. S. R. (2019) · JMIR Medical Informatics · doi:10.2196/14993 · Cited by 66 · see citing works
Liang, S., Ma, J., Wang, G., Shao, J., Li, J., Deng, H., Wang, C., & Li, W. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2022.935080 · Cited by 66 · see citing works
Madaki, M. Y., Kaechele, H., & Bavorová, M. (2023) · Climate Policy · doi:10.1080/14693062.2023.2220672 · Cited by 66 · see citing works
Ntampaka, P., Nyaga, P. N., Gathumbi, J., Tukei, M., & Niragire, F. (2019) · PLoS ONE · doi:10.1371/journal.pone.0210044 · Cited by 66 · see citing works
Dresden, S. M., Hwang, U., Garrido, M. M., Sze, J. J., Kang, R. H., Vargas-Torres, C. C., Courtney, D. M., Loo, G. T., Rosenberg, M. S., & Richardson, L. D. (2019) · Academic Emergency Medicine · doi:10.1111/acem.13880 · Cited by 62 · see citing works
Pediatric Emergency Department Utilization and Reliance by Insurance Coverage in the United States
Schlichting, L. E., Rogers, M. L., Gjelsvik, A., Linakis, J. G., & Vivier, P. M. (2017) · Academic Emergency Medicine · doi:10.1111/acem.13281 · Cited by 62 · see citing works
Manias, E., Cranswick, N., Newall, F. H., Rosenfeld, E., Weiner, C., Williams, A. F., Wong, I. C. K., Borrott, N., Lai, J., & Kinney, S. (2018) · Journal of Paediatrics and Child Health · doi:10.1111/jpc.14193 · Cited by 61 · see citing works
del Río, I. D., & del Valle, E. S. G. (2016) · Violence Against Women · doi:10.1177/1077801216671220 · Cited by 61 · see citing works
Onshore wind and the likelihood of planning acceptance: Learning from a Great Britain context
Harper, M., Anderson, B., James, P. A. B., & Bahaj, A. S. (2019) · Energy Policy · doi:10.1016/j.enpol.2019.01.002 · Cited by 61 · see citing works
Wildfire assessment using machine learning algorithms in different regions
Moghim, S., & Mehrabi, M. (2024) · Fire Ecology · doi:10.1186/s42408-024-00335-2 · Cited by 60 · see citing works
Functional identification of islet cell types by electrophysiological fingerprinting
Briant, L. J. B., Zhang, Q., Vergari, E., Kellard, J. A., Rodriguez, B., Ashcroft, F. M., & Rorsman, P. (2017) · Journal of The Royal Society Interface · doi:10.1098/rsif.2016.0999 · Cited by 59 · see citing works
Almhdawi, K. A., Obeidat, D., Kanaan, S. F., Hajela, N., Bsoul, M., Arabiat, A., Alazrai, A., Jaber, H., & Alrabbaie, H. (2021) · Work · doi:10.3233/wor-205276 · Cited by 59 · see citing works
Survey on AI Applications for Product Quality Control and Predictive Maintenance in Industry 4.0
Johanesa, T. V. A., Equeter, L., & Mahmoudi, S. A. (2024) · Electronics · doi:10.3390/electronics13050976 · Cited by 59 · see citing works
Big Data Analytics Framework for Decision-Making in Sports Performance Optimization
Mănescu, D. C. (2025) · Data · doi:10.3390/data10070116 · Cited by 59 · see citing works
Dermatologist-like explainable AI enhances melanoma diagnosis accuracy: eye-tracking study
Chanda, T., Haggenmueller, S., Bucher, T.-C., Holland‐Letz, T., Kittler, H., Tschandl, P., Heppt, M. V., Berking, C., Utikal, J. S., Schilling, B., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-59532-5 · Cited by 57 · see citing works
Correlation Judgment and Visualization Features: A Comparative Study
Yang, F., Harrison, L., Rensink, R. A., Franconeri, S., & Chang, R. (2018) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2018.2810918 · Cited by 57 · see citing works
Shams, M. Y., El‐kenawy, E. M., Ibrahim, A. F., & Elshewey, A. M. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.104908 · Cited by 56 · see citing works
Evaluation and mapping of predicted future land use changes using hybrid models in a coastal area
Ahmad, H., Abdallah, M., Jose, F., Elzain, H. E., Bhuyan, M. S., Shoemaker, D. J., & Selvam, S. (2023) · Ecological Informatics · doi:10.1016/j.ecoinf.2023.102324 · Cited by 56 · see citing works
Katzilieris, K., Vlahogianni, E. I., & Wang, H. (2022) · Safety Science · doi:10.1016/j.ssci.2022.105799 · Cited by 55 · see citing works
Gómez, C. C. O., Herrera, R. F., Prieto-Osorio, J. M., & Pellicer, E. (2024) · Ain Shams Engineering Journal · doi:10.1016/j.asej.2024.102749 · Cited by 55 · see citing works
Hua, Y., Stead, T. S., George, A., & Ganti, L. (2025) · Academic Medicine & Surgery · doi:10.62186/001c.131964 · Cited by 55 · see citing works
The ability of different imputation methods for missing values in mental measurement questionnaires
Xu, X., Xia, L., Zhang, Q., Wu, S., Wu, M., & Liu, H. (2020) · BMC Medical Research Methodology · doi:10.1186/s12874-020-00932-0 · Cited by 55 · see citing works
Ruiz‐Cantero, M. T., Guijarro-Garví, M., Bean, D. R., Riera, J. R. M., & Fernández‐Sáez, J. (2019) · Health & Place · doi:10.1016/j.healthplace.2019.05.012 · Cited by 53 · see citing works
Botngård, A., Eide, A. H., Mosqueda, L. A., Blekken, L. E., & Malmedal, W. (2021) · BMC Health Services Research · doi:10.1186/s12913-021-06227-4 · Cited by 53 · see citing works
European Journal of Interdisciplinary Studies
(2018) · European Journal of Interdisciplinary Studies · doi:10.24818/ejis · Cited by 53 · see citing works
Çallı, L. (2022) · International Journal of Bank Marketing · doi:10.1108/ijbm-08-2022-0351 · Cited by 52 · see citing works
Hong, Z., Zhang, C., Kong, D., Qi, Z., & He, Y. (2021) · Infrared Physics & Technology · doi:10.1016/j.infrared.2021.103666 · Cited by 52 · see citing works
Wang, L., Fan, R., Zhang, C., Hong, L., Zhang, T., Chen, Y., Liu, K., Wang, Z., & Zhong, J. (2020) · Patient Preference and Adherence · doi:10.2147/ppa.s253732 · Cited by 49 · see citing works
Holzinger, F., Oslislo, S., Möckel, M., Schenk, L., Pigorsch, M., & Heintze, C. (2020) · BMC Health Services Research · doi:10.1186/s12913-020-05689-2 · Cited by 48 · see citing works
Dsouza, V. S., Leyens, L., Kurian, J. R., Brand, A. M., & Brand, H. (2025) · Research in Social and Administrative Pharmacy · doi:10.1016/j.sapharm.2025.02.008 · Cited by 48 · see citing works
Late talkers and later language outcomes: Predicting the different language trajectories
Armstrong, R. M., Scott, J. G., Whitehouse, A., Copland, D. A., McMahon, K. L., & Arnott, W. L. (2017) · International Journal of Speech-Language Pathology · doi:10.1080/17549507.2017.1296191 · Cited by 48 · see citing works
Matteo, D. D., Fotinos, K., Lokuge, S., Mason, G. E., Sternat, T., Katzman, M. A., & Rose, J. (2021) · Journal of Medical Internet Research · doi:10.2196/28918 · Cited by 48 · see citing works
Non-linear relationships in clinical research
Chesnaye, N. C., van Diepen, M., Dekker, F. W., Zoccali, C., Jager, K. J., & Stel, V. S. (2024) · Nephrology Dialysis Transplantation · doi:10.1093/ndt/gfae187 · Cited by 47 · see citing works
Renal growth in isolated methylmalonic acidemia
Kruszka, P. S., Manoli, I., Sloan, J. L., Kopp, J. B., & Venditti, C. P. (2013) · Genetics in Medicine · doi:10.1038/gim.2013.42 · Cited by 47 · see citing works
Mozaffari, M. H., & Tay, L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.10575 · Cited by 47 · see citing works
Lortie, C. J., Filazzola, A., & Sotomayor, D. A. (2015) · Functional Ecology · doi:10.1111/1365-2435.12530 · Cited by 46 · see citing works
Macusi, E. D., Liguez, A. K. O., Macusi, E. S., & Digal, L. N. (2021) · Marine Policy · doi:10.1016/j.marpol.2021.104578 · Cited by 46 · see citing works
Hamidi, F., Gilani, N., Belaghi, R. A., Yaghoobi, H., Babaei, E., Sarbakhsh, P., & Malakouti, J. (2023) · Frontiers in Digital Health · doi:10.3389/fdgth.2023.1187578 · Cited by 45 · see citing works
Fitzpatrick, C., Saritha, R., & Knödler, M. (2016) · Accident Analysis & Prevention · doi:10.1016/j.aap.2016.09.017 · Cited by 45 · see citing works
DDoS Attack Detection and Mitigation in SDN using Machine Learning
Khashab, F., Moubarak, J., Feghali, A., & Bassil, C. (2021) · IEEE International Conference on Network Softwarization (NetSoft) · doi:10.1109/netsoft51509.2021.9492558 · Cited by 44 · see citing works
Quantum Machine-Based Decision Support System for the Detection of Schizophrenia from EEG Records
Aksoy, G., Cattan, G., Chakraborty, S., & Karabatak, M. (2024) · Journal of Medical Systems · doi:10.1007/s10916-024-02048-0 · Cited by 44 · see citing works
Effective Credit Risk Prediction Using Ensemble Classifiers With Model Explanation
Aruleba, I., & Sun, Y. (2024) · IEEE Access · doi:10.1109/access.2024.3445308 · Cited by 44 · see citing works
Different MMSE Score Is Associated with Postoperative Delirium in Young-Old and Old-Old Adults
Wu, Y., Shi, Z., Wang, M., Zhu, Y., Li, C., Li, G., Marcantonio, E. R., Xie, Z., & Shen, Y. (2015) · PLoS ONE · doi:10.1371/journal.pone.0139879 · Cited by 44 · see citing works
Developing Interventions for Scaling Up UK Upcycling
Sung, K. K., Cooper, T., & Kettley, S. (2019) · Energies · doi:10.3390/en12142778 · Cited by 44 · see citing works
Leonhardt, M., Granrud, M. D., Bonsaksen, T., & Lien, L. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph191912826 · Cited by 43 · see citing works
Blekken, L. E., Nakrem, S., Vinsnes, A. G., Norton, C. S., Mørkved, S., Salvesen, Ø. O., & Gjeilo, K. H. (2016) · Gastroenterology Research and Practice · doi:10.1155/2016/1215746 · Cited by 43 · see citing works
A data-driven explainable case-based reasoning approach for financial risk detection
Li, W., Paraschiv, F., & Sermpinis, G. (2022) · Quantitative Finance · doi:10.1080/14697688.2022.2118071 · Cited by 42 · see citing works
Altham, C., Zhang, H., & Pereira, E. (2024) · PLoS ONE · doi:10.1371/journal.pone.0303644 · Cited by 42 · see citing works
Risk factors for nonunion after traumatic humeral shaft fractures in adults
Olson, J. J., Entezari, V., & Vallier, H. A. (2020) · JSES International · doi:10.1016/j.jseint.2020.06.009 · Cited by 42 · see citing works
Anjum, A., Hossain, S., Hasan, M. T., Christopher, E., Uddin, M. E., & Sikder, M. T. (2022) · BMC Psychiatry · doi:10.1186/s12888-022-04340-0 · Cited by 41 · see citing works
Scalp Hematoma Characteristics Associated With Intracranial Injury in Pediatric Minor Head Injury
Burns, E., Grool, A. M., Klassen, T. P., Correll, R., Jarvis, A., Joubert, G. I., Bailey, B., Chauvin‐Kimoff, L., Pusic, M. V., McConnell, D., et al. (2016) · Academic Emergency Medicine · doi:10.1111/acem.12957 · Cited by 41 · see citing works
Bhattarai, J., Oehlert, M. E., Multon, K. D., & Sumerall, S. W. (2018) · Journal of Aging and Health · doi:10.1177/0898264318781131 · Cited by 40 · see citing works
Improving College Student Success in Organic Chemistry: Impact of an Online Preparatory Course
Fischer, C., Zhou, N., Rodriguez, F., Warschauer, M., & King, S. (2019) · Journal of Chemical Education · doi:10.1021/acs.jchemed.8b01008 · Cited by 40 · see citing works
Diagnosis of pulmonary nodules by DNA methylation analysis in bronchoalveolar lavage fluids
Li, L., Ye, Z., Yang, S., Yang, H., Jin, J., Zhu, Y., Tao, J., Chen, S., Xu, J., Liu, Y., et al. (2021) · Clinical Epigenetics · doi:10.1186/s13148-021-01163-w · Cited by 39 · see citing works
Artificial intelligence-driven prediction of multiple drug interactions
Chen, S., Li, T., Yang, L., Zhai, F., Jiang, X., Xiang, R., & Ling, G. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac427 · Cited by 39 · see citing works
Machine Learning to Predict Outcomes of Endovascular Intervention for Patients With PAD
Li, B., Warren, B. E., Eisenberg, N., Beaton, D., Lee, D. S., Aljabri, B. A., Verma, R., Wijeysundera, D. N., Rotstein, O. D., de Mestral, C., et al. (2024) · JAMA Network Open · doi:10.1001/jamanetworkopen.2024.2350 · Cited by 39 · see citing works
Predicting ICU Mortality in Rheumatic Heart Disease: Comparison of XGBoost and Logistic Regression
Xu, Y., Han, D., Huang, T., Zhang, X., Lu, H., Shen, S. Q., Lyu, J., & Wang, H. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2022.847206 · Cited by 39 · see citing works
Junkkari, A., Häyrinen, A., Rauramaa, T. H., Sintonen, H., Nerg, O., Koivisto, A. M., Roine, R. P., Viinamäki, H. T., Soininen, H. S., Luikku, A. J., et al. (2016) · European Journal of Neurology · doi:10.1111/ene.13130 · Cited by 39 · see citing works
Wei, C., Wang, J., Yu, P., Li, A., Xiong, Z., Yuan, Z., Yu, L., & Luo, J. (2024) · Scientific Reports · doi:10.1038/s41598-024-57711-w · Cited by 38 · see citing works
Pei, H., Li, J., Ma, S., Jiang, J., Li, M., Zou, Q., & Lv, Z. (2023) · Applied Sciences · doi:10.3390/app13052858 · Cited by 38 · see citing works
Thomas, S., & Mehrholz, J. (2018) · BMJ Open · doi:10.1136/bmjopen-2017-020163 · Cited by 37 · see citing works
Modeling undergraduates' selection of course modality: A large sample, multi-discipline study
O'Neill, K., Lopes, N., Nesbit, J. C., Reinhardt, S., & Jayasundera, K. (2020) · The Internet and Higher Education · doi:10.1016/j.iheduc.2020.100776 · Cited by 37 · see citing works
Simo, L. P., Agbor, V. N., Temgoua, F. Z., Fozeu, L. F., Bonghaseh, D. T., Mbonda, A., Yurika, R., Dotse‐Gborgbortsi, W., & Mbanya, D. (2021) · BMC Public Health · doi:10.1186/s12889-021-10403-w · Cited by 37 · see citing works
- Authors
- Stoltzfus, J. C.
- Affiliations
- St. Luke's University Health Network (US)
- Corresponding author
- Stoltzfus, Jill C.
- Venue
- Academic Emergency Medicine
- Publisher
- Wiley
- Published
- 2011-10
- Volume
- 18
- Issue
- 10
- Pages
- 1099-1104
- ISSN
- 1069-6563 (ISSN-L)
- 1553-2712
- OpenAlex
- W1723129825
- Cited by
- 1,290
- References
- 24
- Citations by year
- 2026: 128 · 2025: 231 · 2024: 256 · 2023: 182 · 2022: 149 · 2021: 114 · 2020: 58 · 2019: 50 · 2018: 27 · 2017: 26 · 2016: 29 · 2015: 18 · 2014: 13 · 2013: 6 · 2012: 3
- FWCI
- 6.17
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Academic Emergency Medicine (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Keywords
- logistic regression, binary outcomes, confounding, feature selection, multicollinearity, model building strategy, goodness of fit, diagnostic statistics, odds ratio, internal and external validity
- MeSH terms
- Emergency Medicine, Humans, Research Design, Models, Statistical, Logistic Models, Biomedical Research
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
