Prediction of cardiovascular events by algorithm- and formula-based pulse wave velocity
Bibliographic record
Abstract
BACKGROUND: Carotid-femoral pulse wave velocity (PWV), a marker of arterial stiffness, is a recognized cardiovascular disease risk factor. As measuring PWV is time-consuming, reliable estimation methods have been developed, but their ability to inform cardiovascular risk prediction beyond what is achievable with current clinical risk tools is uncertain. METHODS: This study includes participants aged between 40 and 69 years from the population-based CARTaGENE cohort. PWV estimations (ePWV) were obtained using published formulas (ePWV f ) or algorithmic transformation of pulse waveforms (ePWV algo ) and 10-year cardiovascular risk for each participant was computed using the ASCVD and the SCORE-2 risk equations. Participants were followed during 10 years for major adverse cardiovascular events occurrence (MACE: cardiovascular death, myocardial infarction, stroke). Associations of ePWV f and ePWV algo with MACE were obtained using Cox models adjusted for ASCVD or SCORE-2 in the overall population and in a subpopulation representative of the ePWV f derivation cohort. RESULTS: Of 17 548 eligible participants, 2263 (12.9%) experienced a MACE during follow-up. Both ePWVf and ePWV algo were associated with MACE in unadjusted analyses, but only ePWV algo remained significant after adjustments for ASCVD [hazard ratio (HR) = 1.16 [1.09-1.22]] and SCORE-2 (HR = 1.07 [1.00-1.13]). In contrast, ePWV f was not associated with MACE after adjustment for either risk score, and only after adjustment with ASCVD when it was tested in the subpopulation representative of its derivation cohort. CONCLUSIONS: Algorithm-based PWV improved cardiovascular risk prediction beyond what is achievable from recognized risk equations, whereas the predictive ability of ePWV f may not be generalizable outside of its reference population.
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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 | 0.003 | 0.018 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".