Arterial age as a substitute for chronological age in the AGLA risk function could improve coronary risk prediction
Bibliographic record
Abstract
PRINCIPLES: As a result of the relatively low sensitivity of coronary risk charts, such as the Swiss coronary risk calculator (Arbeitsgruppe Lipide und Atherosklerose, AGLA), for detecting subjects with future myocardial infarction, the performance of arterial age (aa) as a surrogate marker for chronological age (ca) was tested. METHODS: In a practice based sample, burden of carotid plaque was obtained with ultrasound, using total plaque area (TPA). In this derivation cohort, sex-specific 5-year groups of mean TPA were calculated in subjects aged between 35 and 79 years. The arterial age formula was found by fitting an exponential function on these data. AGLAca and AGLAaa were tested externally for their ability to detect 13 myocardial infarctions in 684 subjects (validation cohort). RESULTS: The derivation cohort included 1,500 subjects (mean age 59 ± 9 years, mean TPA 54 ± 52 mm2, 5% diabetics, 43% women). Arterial age was found to be y = 5.4175e0.0426x in men and y = 4.1942e0.0392x in women. Mean 10-year AGLAca coronary risk was comparable to AGLAaa (8% ± 9% vs 9% ± 15%). Receiver operating characteristic (ROC) analysis of AGLAca and AGLAaa results showed areas under the curve of 0.65 (p = 0.041) and 0.78 (p <0.0001), respectively, (p = 0.041 for the difference = 0.13). This finding was also confirmed by a Cox proportional hazards regression model on patients' event-free survival (p = not significant for AGLAca, p = 0.0003 for AGLAaa). CONCLUSIONS: Arterial age derived from TPA could be used instead of chronological age in the AGLA coronary risk function. Further studies on the external validity and cost effectiveness of the additional ultrasound imaging study are necessary.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".