A pooled-analysis of age and sex based coronary artery calcium scores percentiles
Bibliographic record
Abstract
BACKGROUND: Age and sex based coronary artery calcium score (CAC) percentiles have been used to improve coronary artery disease (CAD) risk prediction. However, the main limitation of the CACs percentiles currently in use is that they are often based on single studies. We performed a pooled analysis of all available studies that reported on CAC percentiles, in order to develop more generalizable age and sex nomograms. METHODS: PubMed/Medline and Embase were searched for studies that reported nomograms of age and sex-based CACs percentiles. Studies were included if they reported data collected among asymptomatic individuals without a history of cardiovascular disease. Absolute CACs for each specific percentile stratum were pooled and new percentiles were generated taking into account the sample size of the study. RESULTS: We found 831 studies, of which 12 met the inclusion criteria. Data on CACs percentiles of 134,336 Western and 33,488 Asians were pooled separately, rendering a weighted CACs percentile nomogram available at https://www.calciumscorecalculator.com. Our weighted percentiles differed by up to 24% from the nomograms in use today. CONCLUSIONS: Our pooled age and sex based CACs percentiles based on over 155,000 individuals should provide a measure of risk that is more applicable to a wider population than the ones currently in use and hopefully will lead to better risk assessment and treatment decisions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.014 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".