Meaning of clinical calculator results: a cross-sectional analysis of the MDCalc catalogue
He, S.; Joseph, J. W.; Safari, P.; Akomeh, N.; Goff, A.; Slusarz, P. J.; Mohamed, A.; Lord, S.; Goldstein, J. N.; Raja, A. S.; Kabrhel, C.; Locke, B. W.; Rohlfsen, C.; Liebovitz, D. M.
Show abstract
Objectives To quantify complementary meanings of binary clinical calculator results: information beyond the observed outcome frequency, post-result risk, information direction, and precision. Design Catalogue-based cross-sectional analysis of published aggregate performance data, with a reproducible row-level audit. Setting The 847-calculator MDCalc catalogue and a fixed collection of publicly available reports assembled without a systematic literature search. Participants Evaluations comparing a binary calculator result with a binary clinical outcome using a complete or reconstructable 2 x 2 table. Main outcome measures Percentage reduction in uncertainty and corresponding information gain in bits; risk after positive and negative classifications; the proportion of information from each classification; and whether the upper 95% confidence bound for post-negative risk supported specified thresholds. Results The analysis included 482 evaluations of 407 calculators. The median study size was 422 and the median observed outcome frequency was 17.6%. Results reduced uncertainty by a median 14.9% (interquartile range 6.2%-30.5%; 95% confidence interval 12.1% to 17.2%), corresponding to 0.093 bits. In 329 evaluations (68.3%), one classification supplied more than 60% of average information. PERC reduced uncertainty by 4.3% on average while its negative classification lowered observed risk from 7.6% to 1.0%. Two HEART thresholds in the same cohort shifted the positive-classification information share from 39.1% to 79.8%. Post-negative risk was below 2% in 157 evaluations by point estimate, but the upper 95% confidence bound was below 2% in only 67; 90 of 157 (57%) did not support the apparent threshold. Conclusions Clinical calculator results have no single quantitative meaning. Conventional performance, information added beyond the observed outcome frequency, post-result risk, classification-specific information, and precision provide complementary interpretations. Whether acting on a result improves care remains a separate question.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Use of unstructured text in prognostic clinical prediction models: a systematic review 92%
- Clinical Utility of Automatable Prediction Models for Improving Palliative and End-Of-Life Care Outcomes: Towards Routine Decision Analysis Before Implementation 92%
- Sociodemographic Bias in Large Language Model Clinical Trial Screening 91%
Similar papers in this journal
- Clinical practice guidelines and recommendations in the context of the COVID-19 pandemic: systematic review and critical appraisal 92%
- Large-scale validation of the Prediction model Risk Of Bias ASsessment Tool (PROBAST) using a short form: high risk of bias models show poorer discrimination 92%
- Estimating and Testing an Index of Bias Attributable to Composite Outcomes in Comparative Studies 92%
Similar papers in this journal
- Completeness of reporting of clinical prediction models developed using supervised machine learning: A systematic review 94%
- Approaches in Analyzing Predictors of Trial Failure: A Scoping Review and Meta-epidemiological study 93%
- Investigator-initiated versus industry-sponsored trials – Visibility and relevance of randomized controlled trials in clinical practice guidelines (IMPACT) 92%
Similar papers in this journal
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.