Applied Logistic Regression

Guidelines for reporting mortality and morbidity after cardiac valve interventions

Akins, C. W., Miller, D. C., Turina, M. I., Kouchoukos, N. T., Blackstone, E. H., Grunkemeier, G. L., Takkenberg, J. J. M., David, T. E., Butchart, E. G., Adams, D. H., et al. (2008) · Journal of Thoracic and Cardiovascular Surgery · doi:10.1016/j.jtcvs.2007.12.002 · Cited by 664

Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansas, USA

Ohlmacher, G. C., & Davis, J. C. (2003) · Engineering Geology · doi:10.1016/s0013-7952(03)00069-3 · Cited by 665

Do “Moderate” Drinkers Have Reduced Mortality Risk? A Systematic Review and Meta-Analysis of Alcohol Consumption and All-Cause Mortality

Stockwell, T. R., Zhao, J., Panwar, S., Roemer, A., Naimi, T. S., & Chikritzhs, T. N. (2016) · Journal of Studies on Alcohol and Drugs · doi:10.15288/jsad.2016.77.185 · Cited by 658

ASA class is a reliable independent predictor of medical complications and mortality following surgery

Hackett, N., Oliveira, G. S. D., Jain, U. K., & Kim, J. Y. S. (2015) · International Journal of Surgery · doi:10.1016/j.ijsu.2015.04.079 · Cited by 653

Prediction of Psychosis in Adolescents and Young Adults at High Risk: Results From the Prospective European Prediction of Psychosis Study

Ruhrmann, S., Schultze-Lutter, F., Salokangas, R. K. R., Heinimaa, M., Linszen, D., Dingemans, P., Birchwood, M., Patterson, P., Juckel, G., Heinz, A., et al. (2010) · Archives of General Psychiatry · doi:10.1001/archgenpsychiatry.2009.206 · Cited by 632

Psychological responses matter in returning to preinjury level of sport after anterior cruciate ligament reconstruction surgery

Ardern, C. L., Taylor, N. F., Feller, J. A., Whitehead, T. S., & Webster, K. E. (2013) · The American Journal of Sports Medicine · doi:10.1177/0363546513489284 · Cited by 660

The Elements of Statistical Learning

Sapatinas, T. (2003) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/j.1467-985x.2004.298_11.x · Cited by 635

Impact of Bronchopulmonary Dysplasia, Brain Injury, and Severe Retinopathy on the Outcome of Extremely Low-Birth-Weight Infants at 18 Months

Schmidt, B. K., Asztalos, E. V., Roberts, R. S., Robertson, C. M. T., Sauve, R. S., Whitfield, M. F., & for the Trial of Indomethacin Prophylaxis in Preterms (TIPP) Investigators (2003) · JAMA · doi:10.1001/jama.289.9.1124 · Cited by 630

Sexual orientation, substance use behaviors and substance dependence in the United States

McCabe, S. E., Hughes, T. L., Bostwick, W. B., West, B. T., & Boyd, C. J. (2009) · Addiction · doi:10.1111/j.1360-0443.2009.02596.x · Cited by 632

Determinants of COVID-19 disease severity in patients with cancer

Robilotti, E. V., Babady, N. E., Mead, P. A., Rolling, T., Pérez-Johnston, R., Bernardes, M., Bogler, Y., Caldararo, M. D., Figueroa, C. J., Glickman, M. S., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0979-0 · Cited by 634

Landslide susceptibility assessment using SVM machine learning algorithm

Marjanović, M., Kovačević, M. A., Bajat, B., & Voženílek, V. (2011) · Engineering Geology · doi:10.1016/j.enggeo.2011.09.006 · Cited by 621

Trends in mental illness and suicidality after Hurricane Katrina

Kessler, R. C., Galea, S., Gruber, M. J., Sampson, N. A., Ursano, R. J., & Wessely, S. (2008) · Molecular Psychiatry · doi:10.1038/sj.mp.4002119 · Cited by 617

A method for meta-analysis of molecular association studies

Thakkinstian, A., McElduff, P., D’Este, C. A., Duffy, D. L., & Attia, J. (2004) · Statistics in Medicine · doi:10.1002/sim.2010 · Cited by 615

Validation studies of the health improvement network (THIN) database for pharmacoepidemiology research

Lewis, J. D., Schinnar, R., Bilker, W. B., Wang, X., & Strom, B. L. (2007) · Pharmacoepidemiology and Drug Safety · doi:10.1002/pds.1335 · Cited by 602

Validation of the Atherosclerotic Cardiovascular Disease Pooled Cohort Risk Equations

Muntner, P., Colantonio, L. D., Cushman, M., Goff, D. C., Howard, G., Howard, V. J., Kissela, B., Levitan, E. B., Lloyd-Jones, D. M., & Safford, M. M. (2014) · JAMA · doi:10.1001/jama.2014.2630 · Cited by 603

Disclosure Tone and Shareholder Litigation

Rogers, J. L., Buskirk, A. V., & Zechman, S. L. C. (2011) · The Accounting Review · doi:10.2308/accr-10137 · Cited by 582

Adaptive Networks

Sayed, A. H. (2014) · Proceedings of the IEEE · doi:10.1109/jproc.2014.2306253 · Cited by 599

Goodness-of-fit Test for a Logistic Regression Model Fitted using Survey Sample Data

Archer, K. J., & Lemeshow, S. A. (2006) · The Stata Journal Promoting communications on statistics and Stata · doi:10.1177/1536867x0600600106 · Cited by 592

Keep Calm and Learn Multilevel Logistic Modeling: A Simplified Three-Step Procedure Using Stata, R, Mplus, and SPSS

Sommet, N., & Morselli, D. (2017) · International Review of Social Psychology · doi:10.5334/irsp.90 · Cited by 640

Exposure to Hurricane-Related Stressors and Mental Illness After Hurricane Katrina

Galea, S., Brewin, C. R., Gruber, M., Jones, R. T., King, D. W., King, L. A., McNally, R. J., Ursano, R. J., Petukhova, M., & Kessler, R. C. (2007) · Archives of General Psychiatry · doi:10.1001/archpsyc.64.12.1427 · Cited by 584

Communicating with realism and hope: incurable cancer patients' views on the disclosure of prognosis

Hagerty, R. G., Butow, P. N., Ellis, P. M., Lobb, E. A., Pendlebury, S. C., Leighl, N., Mac Leod, C., & Tattersall, M. H. N. (2005) · Journal of Clinical Oncology · doi:10.1200/jco.2005.11.138 · Cited by 582

Utilization of maternal health care services in Southern India

Navaneetham, K., & Dharmalingam, A. (2002) · Social Science & Medicine · doi:10.1016/s0277-9536(01)00313-6 · Cited by 584

Reducing Hospital Readmission Rates: Current Strategies and Future Directions

Kripalani, S. B., Theobald, C. N., Anctil, B., & Vasilevskis, E. E. (2013) · Annual Review of Medicine · doi:10.1146/annurev-med-022613-090415 · Cited by 627

Application of GIS based data driven evidential belief function model to predict groundwater potential zonation

Nampak, H., Pradhan, B., & Manap, M. A. (2014) · Journal of Hydrology · doi:10.1016/j.jhydrol.2014.02.053 · Cited by 572

The Sarbanes–Oxley Act and firms’ going-private decisions

Engel, E., Hayes, R. M., & Wang, X. (2006) · Journal of Accounting and Economics · doi:10.1016/j.jacceco.2006.07.002 · Cited by 564

A Pooled Analysis of Advanced Colorectal Neoplasia Diagnoses After Colonoscopic Polypectomy

Martı́nez, M. E., Baron, J. A., Lieberman, D. A., Schatzkin, A. G., Lanza, E., Winawer, S. J., Zauber, A. G., Jiang, R., Ahnen, D. J., Bond, J. H., et al. (2008) · Gastroenterology · doi:10.1053/j.gastro.2008.12.007 · Cited by 565

Prevalence, Demographics, and Psychological Associations of Sleep Disruption in Patients With Cancer: University of Rochester Cancer Center–Community Clinical Oncology Program

Palesh, O. G., Roscoe, J. A., Mustian, K. M., Roth, T., Savard, J., Ancoli-Israel, S., Heckler, C., Purnell, J. Q., Janelsins, M. C., & Morrow, G. R. (2010) · Journal of Clinical Oncology · doi:10.1200/jco.2009.22.5011 · Cited by 557

Breakfast skipping and health-compromising behaviors in adolescents and adults

Keski‐Rahkonen, A., Kaprio, J. A., Rissanen, A., Virkkunen, M. E., & Rose, R. J. (2003) · European Journal of Clinical Nutrition · doi:10.1038/sj.ejcn.1601618 · Cited by 558

Risk Factors for Chronic Pain Following Breast Cancer Surgery: A Prospective Study

Poleshuck, E. L., Katz, J., Andrus, C. H., Hogan, L. A., Jung, B. F., Kulick, D. I., & Dworkin, R. H. (2006) · Journal of Pain · doi:10.1016/j.jpain.2006.02.007 · Cited by 552

Social Democracy Inside Out

Rueda, D. (2007) · doi:10.1093/acprof:oso/9780199216352.001.0001 · Cited by 622

An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps

Nefeslioğlu, H. A., Gökçeoğlu, C., & Sönmez, H. (2008) · Engineering Geology · doi:10.1016/j.enggeo.2008.01.004 · Cited by 553

OptimalCutpoints : An R Package for Selecting Optimal Cutpoints in Diagnostic Tests

López‐Ratón, M., Rodríguez‐Álvarez, M. X., Cadarso-Suárez, C., & Gudé, F. (2014) · Journal of Statistical Software · doi:10.18637/jss.v061.i08 · Cited by 556

Statistical analysis of amplified fragment length polymorphism data: a toolbox for molecular ecologists and evolutionists

Bonin, A., Ehrich, D., & Manel, S. (2007) · Molecular Ecology · doi:10.1111/j.1365-294x.2007.03435.x · Cited by 546

Power of multifactor dimensionality reduction for detecting gene‐gene interactions in the presence of genotyping error, missing data, phenocopy, and genetic heterogeneity

Ritchie, M. D., Hahn, L. W., & Moore, J. H. (2003) · Genetic Epidemiology · doi:10.1002/gepi.10218 · Cited by 549

Age differences in major depression: Results from the national comorbidity survey replication (NCS-R)

Kessler, R. C., Birnbaum, H., Bromet, E., Hwang, I., Sampson, N., & Shahly, V. (2010) · Psychological Medicine · doi:10.1017/s0033291709990213 · Cited by 542

Examples of mixed-effects modeling with crossed random effects and with binomial data

Quené, H., & van den Bergh, H. (2008) · Journal of Memory and Language · doi:10.1016/j.jml.2008.02.002 · Cited by 534

A Network-based Approach on Opportunity Recognition

Arenius, P. M., & Clercq, D. D. (2005) · Small Business Economics · doi:10.1007/s11187-005-1988-6 · Cited by 535

Household food waste behaviour in EU-27 countries: a multilevel analysis

Secondi, L., Principato, L., & Laureti, T. (2015) · Food Policy · doi:10.1016/j.foodpol.2015.07.007 · Cited by 529

Factors influencing the use of green space: Results from a Danish national representative survey

Schipperijn, J., Ekholm, O., Stigsdotter, U. K., Toftager, M., Bentsen, P., Kamper‐Jørgensen, F., & Randrup, T. B. (2010) · Landscape and Urban Planning · doi:10.1016/j.landurbplan.2009.12.010 · Cited by 555

Identifying critically-ill patients who will benefit most from nutritional therapy: Further validation of the “modified NUTRIC” nutritional risk assessment tool

Rahman, A., Hasan, R. M., Agarwala, R., Martin, C. M., Day, A. G., & Heyland, D. K. (2015) · Clinical Nutrition · doi:10.1016/j.clnu.2015.01.015 · Cited by 550

Predicting defect-prone software modules using support vector machines

Elish, K. O., & Elish, M. O. (2007) · Journal of Systems and Software · doi:10.1016/j.jss.2007.07.040 · Cited by 526

Non-high-density lipoprotein cholesterol and apolipoprotein B in the prediction of coronary heart disease in men

Pischon, T., Girman, C. J., Sacks, F. M., Rifai, N., Stampfer, M. J., & Rimm, E. B. (2005) · Circulation · doi:10.1161/circulationaha.104.532499 · Cited by 514

Do personal characteristics and cultural values that promote innovation, quality, and efficiency compete or complement each other?

Miron, E., Erez, M., & Naveh, E. (2004) · Journal of Organizational Behavior · doi:10.1002/job.237 · Cited by 736

performance: Assessment of Regression Models Performance

Lüdecke, D., Makowski, D., Ben‐Shachar, M. S., Patil, I., Waggoner, P., Wiernik, B. M., & Thériault, R. (2019) · doi:10.32614/cran.package.performance · Cited by 512

A Multiple Motives Approach to Tobacco Dependence: The Wisconsin Inventory of Smoking Dependence Motives (WISDM-68).

Piper, M. E., Piasecki, T. M., Federman, E. B., Bolt, D. M., Smith, S. S., Fiore, M. C., & Baker, T. B. (2004) · Journal of Consulting and Clinical Psychology · doi:10.1037/0022-006x.72.2.139 · Cited by 510

Evaluating the risk of ovarian cancer before surgery using the ADNEX model to differentiate between benign, borderline, early and advanced stage invasive, and secondary metastatic tumours: prospective multicentre diagnostic study

Van Calster, B., Van Hoorde, K., Valentin, L., Testa, A. C., Fischerova, D., Van Holsbeke, C., Savelli, L., Franchi, D., Epstein, E., Kaijser, J., et al. (2014) · BMJ · doi:10.1136/bmj.g5920 · Cited by 510

Basic statistical analysis in genetic case-control studies

Clarke, G. M., Anderson, C. A., Pettersson, F. H., Cardon, L. R., Morris, A. P., & Zondervan, K. T. (2011) · Nature Protocols · doi:10.1038/nprot.2010.182 · Cited by 531

Population‐based assessment of adverse events associated with long‐term glucocorticoid use

Curtis, J. R., Westfall, A. O., Allison, J., Bijlsma, J. W., Freeman, A., George, V., Kovac, S. H., Spettell, C. M., & Saag, K. G. (2006) · Arthritis Care & Research · doi:10.1002/art.21984 · Cited by 595

Cancer Patient Preferences for Communication of Prognosis in the Metastatic Setting

Hagerty, R. G., Butow, P. N., Ellis, P. A., Lobb, E. A., Pendlebury, S. C., Leighl, N. B., Goldstein, D., Lo, S. K., & Tattersall, M. H. N. (2004) · Journal of Clinical Oncology · doi:10.1200/jco.2004.04.095 · Cited by 504

Individual and societal effects of mental disorders on earnings in the United States: results from the national comorbidity survey replication

Kessler, R. C., Heeringa, S., Lakoma, M. D., Petukhova, M., Rupp, A. E., Schoenbaum, M., Wang, P. S., & Zaslavsky, A. M. (2008) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2008.08010126 · Cited by 486

Investigating the sources of process innovation among UK manufacturing firms

Reichstein, T., & Salter, A. J. (2006) · Industrial and Corporate Change · doi:10.1093/icc/dtl014 · Cited by 522

Genetic Modifiers of Lung Disease in Cystic Fibrosis

Drumm, M. L., Konstan, M. W., SCHLUCHTER, M. D., Handler, A., Pace, R. G., Zou, F., Zariwala, M. A., Fargo, D. C., Xu, A., Dunn, J. M., et al. (2005) · New England Journal of Medicine · doi:10.1056/nejmoa051469 · Cited by 498

Screening for Barrett’s esophagus in colonoscopy patients with and without heartburn

Rex, D. K., Cummings, O. W., Shaw, M., Cumings, M., Wong, R. K. H., Vasudeva, R. S., Dunne, D., Rahmani, E. Y., & Helper, D. J. (2003) · Gastroenterology · doi:10.1053/j.gastro.2003.09.030 · Cited by 501

Transsphenoidal Surgery for Pituitary Tumors in the United States, 1996–2000: Mortality, Morbidity, and the Effects of Hospital and Surgeon Volume

Barker, F. G., Klibanski, A., & Swearingen, B. (2003) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2003-030461 · Cited by 501

A tutorial on calibration measurements and calibration models for clinical prediction models

Huang, Y., Li, W., Macheret, F., Gabriel, R. A., & Ohno-Machado, L. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocz228 · Cited by 486

The promise of implementing machine learning in earthquake engineering: A state‐of‐the‐art review

Xie, Y., Sichani, M. E., Padgett, J. E., & DesRoches, R. (2020) · Earthquake Spectra · doi:10.1177/8755293020919419 · Cited by 528

Medication use in pregnancy: a cross-sectional, multinational web-based study

Lupattelli, A., Spigset, O., Twigg, M., Zagorodnikova, K., Mårdby, A., Moretti, M., Drozd, M., Panchaud, A., Hämeen-Anttila, K., Rieutord, A., et al. (2014) · BMJ Open · doi:10.1136/bmjopen-2013-004365 · Cited by 435

PREDICTING WHO REOFFENDS: THE NEGLECTED ROLE OF NEIGHBORHOOD CONTEXT IN RECIDIVISM STUDIES*

Kubrin, C. E., & Stewart, E. (2006) · Criminology · doi:10.1111/j.1745-9125.2006.00046.x · Cited by 496

Evaluating credit risk and loan performance in online Peer-to-Peer (P2P) lending

Emekter, R., Tu, Y., Jirasakuldech, B., & Lu, M. (2014) · Applied Economics · doi:10.1080/00036846.2014.962222 · Cited by 507

Fundamentals, materials, and machine learning of polymer electrolyte membrane fuel cell technology

Wang, Y., Seo, B., Wang, B., Zamel, N., Jiao, K., & Adroher, X. C. (2020) · Energy and AI · doi:10.1016/j.egyai.2020.100014 · Cited by 508

A comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods: a case study at İzmir, Turkey

Akgün, A. (2011) · Landslides · doi:10.1007/s10346-011-0283-7 · Cited by 488

Seeking Shelter: Empirically Modeling Tax Shelters Using Financial Statement Information

Lisowsky, P. (2010) · The Accounting Review · doi:10.2308/accr.2010.85.5.1693 · Cited by 484

Guidelines for Reporting Mortality and Morbidity After Cardiac Valve Interventions

Akins, C. W., Miller, D. C., Turina, M. I., Kouchoukos, N. T., Blackstone, E. H., Grunkemeier, G. L., Takkenberg, J. J. M., David, T. E., Butchart, E. G., Adams, D. H., et al. (2008) · The Annals of Thoracic Surgery · doi:10.1016/j.athoracsur.2007.12.082 · Cited by 482

Screening diabetic patients for non-alcoholic fatty liver disease with controlled attenuation parameter and liver stiffness measurements: a prospective cohort study

Kwok, R., Choi, K. C., Wong, G. L., Zhang, Y., Chan, H. L.-Y., Luk, A. O. Y., Shu, S. S., Chan, A. W. H., Yeung, M. W., Chan, J. C. N., et al. (2015) · Gut · doi:10.1136/gutjnl-2015-309265 · Cited by 489

Logistic Regression Model to Distinguish Between the Benign and Malignant Adnexal Mass Before Surgery: A Multicenter Study by the International Ovarian Tumor Analysis Group

Timmerman, D., Testa, A. C., Bourne, T., Ferrazzi, E. M., Ameye, L., Konstantinovic, M. L., CALSTER, B. V., Collins, W. P., Vergote, I. B., Huffel, S. V., et al. (2005) · Journal of Clinical Oncology · doi:10.1200/jco.2005.01.7632 · Cited by 480

Behavioral adjustments of African herbivores to predation risk by lions: Spatiotemporal variations influence habitat use

Valeix, M., Loveridge, A. J., Chamaillé-Jammes, S., Davidson, Z., Murindagomo, F., Fritz, H., & Macdonald, D. W. (2009) · Ecology · doi:10.1890/08-0606.1 · Cited by 478

Tranexamic acid use and postoperative outcomes in patients undergoing total hip or knee arthroplasty in the United States: retrospective analysis of effectiveness and safety

Poeran, J., Rasul, R., Suzuki, S., Danninger, T., Mazumdar, M., Opperer, M., Boettner, F., & Memtsoudis, S. G. (2014) · BMJ · doi:10.1136/bmj.g4829 · Cited by 477

Discriminative Least Squares Regression for Multiclass Classification and Feature Selection

Xiang, S., Nie, F., Meng, G., Pan, C., & Zhang, C. (2012) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2012.2212721 · Cited by 476

American Academy of Clinical Neuropsychology (AACN) 2021 consensus statement on validity assessment: Update of the 2009 AACN consensus conference statement on neuropsychological assessment of effort, response bias, and malingering

Sweet, J. J., Heilbronner, R. L., Morgan, J. E., Larrabee, G. J., Rohling, M. L., Boone, K. B., Kirkwood, M. W., Schroeder, R. W., Suhr, J. A., & Participants, C. (2021) · The Clinical Neuropsychologist · doi:10.1080/13854046.2021.1896036 · Cited by 495

Want to model a species niche? A step-by-step guideline on correlative ecological niche modelling

Sillero, N., Arenas-Castro, S., Enriquez‐Urzelai, U., Vale, C. G., Sousa-Guedes, D., Martínez-Freiría, F., Real, R., & Barbosa, A. M. (2021) · Ecological Modelling · doi:10.1016/j.ecolmodel.2021.109671 · Cited by 456

C-Reactive Protein/Albumin Ratio Predicts 90-Day Mortality of Septic Patients

Ranzani, O., Zampieri, F. G., Forte, D. N., Azevedo, L. C. P., & Park, M. (2013) · PLoS ONE · doi:10.1371/journal.pone.0059321 · Cited by 482

A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan Mountains, SW Turkey

Özdemir, A., & Altural, T. (2012) · Journal of Asian Earth Sciences · doi:10.1016/j.jseaes.2012.12.014 · Cited by 472

Derivation and Validation of a Simplified Predictive Index for Renal Replacement Therapy After Cardiac Surgery

Wijeysundera, D. N., Karkouti, K., Dupuis, J., Rao, V., Chan, C. T., Granton, J. T., & Beattie, W. S. (2007) · JAMA · doi:10.1001/jama.297.16.1801 · Cited by 471

Adults with autism overestimate the volatility of the sensory environment

Lawson, R. P., Mathys, C., & Rees, G. (2017) · Nature Neuroscience · doi:10.1038/nn.4615 · Cited by 470

A MULTI‐SCALE TEST OF THE FORAGE MATURATION HYPOTHESIS IN A PARTIALLY MIGRATORY UNGULATE POPULATION

Hebblewhite, M., Merrill, E. H., & McDermid, G. J. (2008) · Ecological Monographs · doi:10.1890/06-1708.1 · Cited by 469

Unwanted and wanted exposure to online pornography in a national sample of youth internet users

Wolak, J., Mitchell, K., & Finkelhor, D. (2007) · Pediatrics · doi:10.1542/peds.2006-1891 · Cited by 467

Risk factors for surgical site infection in spinal surgery

Olsen, M. A. A., Mayfield, J. L., Lauryssen, C. L., Polish, L. B., Jones, M., Vest, J. R., & Fraser, V. J. (2003) · Journal of Neurosurgery Spine · doi:10.3171/spi.2003.98.2.0149 · Cited by 468

Lipid-Lowering Therapy and In-Hospital Mortality Following Major Noncardiac Surgery

Lindenauer, P. K. (2004) · JAMA · doi:10.1001/jama.291.17.2092 · Cited by 467

Interactions with window openings by office occupants

Haldi, F., & Robinson, D. (2009) · Building and Environment · doi:10.1016/j.buildenv.2009.03.025 · Cited by 464

Preeclampsia, gestational hypertension and intrauterine growth restriction, related or independent conditions?

Villar, J., Carroli, G., WOJDYLA, D. M., Abalos, E. J., Giordano, D., Ba’aqeel, H., Farnot, U., Bergsjø, P., Bakketeig, L. S., Lumbiganon, P., et al. (2006) · American Journal of Obstetrics and Gynecology · doi:10.1016/j.ajog.2005.10.813 · Cited by 464

Prospective Breast Cancer Risk Prediction Model for Women Undergoing Screening Mammography

Barlow, W. E., White, E., Ballard-Barbash, R. R., Vacek, P. M., Titus-Ernstoff, L. T., Carney, P. A., Tice, J. A., Buist, D. S. M., Geller, B. M., Rosenberg, R., et al. (2006) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djj331 · Cited by 462

Are there better indices for evaluation purposes than the h index? A comparison of nine different variants of the h index using data from biomedicine

Bornmann, L., Mutz, R., & Daniel, H. (2008) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.20806 · Cited by 462

Delirium in an adult acute hospital population: predictors, prevalence and detection

Ryan, D. J., O’Regan, N., Ó’Caoimh, R., Clare, J., O’Connor, M., Leonard, M. M., Mcfarland, J. W., Tighe, S., O’Sullivan, K., Trzepacz, P. T., et al. (2013) · BMJ Open · doi:10.1136/bmjopen-2012-001772 · Cited by 461

Spatial interactions among ecosystem services in an urbanizing agricultural watershed

Qiu, J., & Turner, M. G. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1310539110 · Cited by 488

A spatial analysis method (SAM) to detect candidate loci for selection: towards a landscape genomics approach to adaptation

Joost, S., Bonin, A., Bruford, M. W., Després, L., Conord, C., Erhardt, G., & Taberlet, P. (2007) · Molecular Ecology · doi:10.1111/j.1365-294x.2007.03442.x · Cited by 461

Cervical pessary in pregnant women with a short cervix (PECEP): an open-label randomised controlled trial

Goya, M., Pratcorona, L., Merced, C., Rodó, C., Valle, L., Romero, A., Juan, M., Rodríguez, A., Muñoz, B., Santacruz, B., et al. (2012) · The Lancet · doi:10.1016/s0140-6736(12)60030-0 · Cited by 470

Accounting for individual‐specific variation in habitat‐selection studies: Efficient estimation of mixed‐effects models using Bayesian or frequentist computation

Muff, S., Signer, J., & Fieberg, J. (2019) · Journal of Animal Ecology · doi:10.1111/1365-2656.13087 · Cited by 453

Why do firms both make and buy? An investigation of concurrent sourcing

Parmigiani, A. (2007) · Strategic Management Journal · doi:10.1002/smj.580 · Cited by 466

Spatial pattern of greenspace affects land surface temperature: evidence from the heavily urbanized Beijing metropolitan area, China

Li, X., Zhou, W., Ouyang, Z., Xu, W., & Zheng, H. (2012) · Landscape Ecology · doi:10.1007/s10980-012-9731-6 · Cited by 456

How is the minimal clinically important difference established in health-related quality of life instruments? Review of anchors and methods

Mouelhi, Y., Jouve, E., Castelli, C., & Gentile, S. (2020) · Health and Quality of Life Outcomes · doi:10.1186/s12955-020-01344-w · Cited by 397

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