Back

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.

2026-09-10 health informatics
10.64898/2026.09.08.26362556 medRxiv
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.

50% of probability mass above

"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.