ArticleBMC medicine2023
Evidence of questionable research practices in clinical prediction models.
Article in BMC medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 74 papers, 8 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
74 citing papers in PubMed, 8 syntheses or guidelines pooled it.
- Machine learning models for injury risk prediction in football players: A systematic review of predictors, performance, practical applications, and limitations.The Journal of international medical research · 2026Pooled it
- Prediction models for longitudinal trajectories of depression and anxiety: a systematic review.Journal of affective disorders · 2026Pooled it
- Diagnostic accuracy of gastrointestinal ultrasound in predicting enteral feeding intolerance: a systematic review and meta-analysis.Frontiers in nutrition · 2026Pooled it
- Toward Explainable Carcinogenicity Prediction: An Integrated Cheminformatics Approach and Consensus Framework for Possibly Carcinogenic Chemicals.Journal of chemical information and modeling · 2025Guideline
- Evaluating predictive performance, validity, and applicability of machine learning models for predicting HIV treatment interruption: a systematic review.BMC global and public health · 2025Pooled it
- Can machine learning be a reliable tool for predicting hematoma progression following traumatic brain injury? A systematic review and meta-analysis.Neuroradiology · 2025Pooled it
- Substantial heterogeneity in trauma triage tool characteristic operationalization for identification of major trauma: a hybrid systematic review.European journal of trauma and emergency surgery : official publication of the European Trauma Society · 2025Pooled it
- Mortality prediction of heart transplantation using machine learning models: a systematic review and meta-analysis.Frontiers in artificial intelligence · 2025Pooled it
- Development of a clinical prediction model for falls in individuals with COPD.BMJ open respiratory research · 2025Trial
- Who Is Getting the Help They Need? An AI-Driven Study of Intersectional Disparities in Mental Health Service Utilization Among Young Adults with Suicidal Ideation.Prevention science : the official journal of the Society for Prevention Research · 2026Article
- Comparative external validation of ICH prognostic scores for discharge outcomes in the RES-Q multinational quality registry.European stroke journal · 2026Article
- Judgment behaviors differentiate depression from anxiety.Scientific reports · 2026Article
- A Clinically Accessible Nomogram for Identifying Likely Psoriatic Arthritis Among Patients with Psoriasis: A Multicenter Cross-Sectional Study.Journal of clinical medicine · 2026Article
- Role of extracellular volume fraction determined by dual-layer spectral detector CT in predicting the tumor grade of pancreatic neuroendocrine neoplasms.Japanese journal of radiology · 2026Article
- Harnessing Machine Learning and Electronic Health Record Data to Improve Asthma Management.Current allergy and asthma reports · 2026Review
- Determinants of Rabies Post-Exposure Prophylaxis Compliance in Bangladesh: Informing Policy for Elimination by 2030.Tropical medicine and infectious disease · 2026Article
- Prediction Models and Risk Factors for Steroid Resistance in Children with Nephrotic Syndrome: A Systematic Review and Meta-Analysis.Journal of clinical medicine · 2026Review
- Validation of the Patient Health Questionnaire-2 (PHQ-2) in Detecting Depression Among Older Adults.Aging medicine (Milton (N.S.W)) · 2026Article
- Identifying risk profile for adolescent e-cigarette use: A sex-stratified machine learning analysis.Drug and alcohol dependence reports · 2026Article
- Discriminative Index: A Novel Indicator for Evaluating Machine Learning Algorithms in Laboratory Medicine.Diagnostics (Basel, Switzerland) · 2026Article
14 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
backgroundClinical prediction models are widely used in health and medical research. The area under the receiver operating characteristic curve (AUC) is a frequently used estimate to describe the discriminatory ability of a clinical prediction model. The AUC is often interpreted relative to thresholds, with "good" or "excellent" models defined at 0.7, 0.8 or 0.9. These thresholds may create targets that result in "hacking", where researchers are motivated to re-analyse their data until they achieve a "good" result.
methodsWe extracted AUC values from PubMed abstracts to look for evidence of hacking. We used histograms of the AUC values in bins of size 0.01 and compared the observed distribution to a smooth distribution from a spline.
resultsThe distribution of 306,888 AUC values showed clear excesses above the thresholds of 0.7, 0.8 and 0.9 and shortfalls below the thresholds.
conclusionsThe AUCs for some models are over-inflated, which risks exposing patients to sub-optimal clinical decision-making. Greater modelling transparency is needed, including published protocols, and data and code sharing.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.