Meaning of clinical calculator results: a cross-sectional analysis of the MDCalc catalogue
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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.
Key messages
What is already known on this topic
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Clinical calculators are conventionally described using sensitivity, specificity, predictive values, likelihood ratios, discrimination, and calibration, but these measures answer different questions about a result.
What this study adds
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Across 482 evaluations, calculator results removed a median 14.9% of starting uncertainty; one classification supplied more than 60% of average information in 329 evaluations.
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Of 157 evaluations with a post-negative point estimate below 2%, only 67 had an upper 95% confidence bound below 2%.
How this study might affect research, practice or policy
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Reports of clinical calculators could pair conventional accuracy with starting risk, post-result risks and intervals, and, when average learning matters, uncertainty reduction and its direction; none alone establishes clinical benefit.
