Meaning of clinical calculator results: a cross-sectional analysis of the MDCalc catalogue
- 1Division of AI, Department of Emergency Medicine, Massachusetts General Hospital, Mass General Brigham, Boston, Massachusetts, USA
Abstract
ObjectivesTo quantify complementary meanings of binary clinical calculator results: information beyond the observed outcome frequency, post-result risk, information direction, and precision.
DesignCatalogue-based cross-sectional analysis of published aggregate performance data, with a reproducible row-level audit.
SettingThe 847-calculator MDCalc catalogue and a fixed collection of publicly available reports assembled without a systematic literature search.
ParticipantsEvaluations comparing a binary calculator result with a binary clinical outcome using a complete or reconstructable 2 x 2 table.
Main outcome measuresPercentage 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.
ResultsThe 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.
ConclusionsClinical 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 messagesO_ST_ABSWhat is already known on this topicC_ST_ABSO_LIClinical calculators are conventionally described using sensitivity, specificity, predictive values, likelihood ratios, discrimination, and calibration, but these measures answer different questions about a result. C_LI
What this study addsO_LIAcross 482 evaluations, calculator results removed a median 14.9% of starting uncertainty; one classification supplied more than 60% of average information in 329 evaluations. C_LIO_LIOf 157 evaluations with a post-negative point estimate below 2%, only 67 had an upper 95% confidence bound below 2%. C_LI
How this study might affect research, practice or policyO_LIReports 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. C_LI
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BibTeX
@misc{he2026meaning,
title = {{Meaning of clinical calculator results: a cross-sectional analysis of the MDCalc catalogue}},
author = {S. He and J. W. Joseph and P. Safari and N. Akomeh and A. Goff and P. J. Slusarz and A. Mohamed and S. Lord and J. N. Goldstein and A. S. Raja and C. Kabrhel and B. W. Locke and C. Rohlfsen and D. M. Liebovitz},
howpublished = {medRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.08.26362556},
url = {https://www.medrxiv.org/content/10.64898/2026.09.08.26362556v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Meaning of clinical calculator results: a cross-sectional analysis of the MDCalc catalogue
AU - S. He
AU - J. W. Joseph
AU - P. Safari
AU - N. Akomeh
AU - A. Goff
AU - P. J. Slusarz
AU - A. Mohamed
AU - S. Lord
AU - J. N. Goldstein
AU - A. S. Raja
AU - C. Kabrhel
AU - B. W. Locke
AU - C. Rohlfsen
AU - D. M. Liebovitz
PY - 2026
DA - 2026/09/08
PB - medRxiv
DO - 10.64898/2026.09.08.26362556
UR - https://www.medrxiv.org/content/10.64898/2026.09.08.26362556v1
AB - ObjectivesTo quantify complementary meanings of binary clinical calculator results: information beyond the observed outcome frequency, post-result risk, information direction, and precision. DesignCatalogue-based cross-sectional analysis of published aggregate performance data, with a reproducible row-level audit. SettingThe 847-calculator MDCalc catalogue and a fixed collection of publicly available reports assembled without a systematic literature search. ParticipantsEvaluations comparing a binary calculator result with a binary clinical outcome using a complete or reconstructable 2 x 2 table. Main outcome measuresPercentage 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. ResultsThe 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. ConclusionsClinical 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 messagesO_ST_ABSWhat is already known on this topicC_ST_ABSO_LIClinical calculators are conventionally described using sensitivity, specificity, predictive values, likelihood ratios, discrimination, and calibration, but these measures answer different questions about a result. C_LI What this study addsO_LIAcross 482 evaluations, calculator results removed a median 14.9% of starting uncertainty; one classification supplied more than 60% of average information in 329 evaluations. C_LIO_LIOf 157 evaluations with a post-negative point estimate below 2%, only 67 had an upper 95% confidence bound below 2%. C_LI How this study might affect research, practice or policyO_LIReports 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. C_LI
ER -