Health status and cognitive function for risk stratification in chronic coronary and peripheral artery disease
Bibliographic record
Abstract
BACKGROUND AND AIMS: It is unclear whether health status and cognitive function assessments can augment traditional coronary artery disease (CAD) and peripheral artery disease (PAD) biomedical risk prediction frameworks. We examined the association between health status and cognitive function and subsequent adverse cardiovascular and limb events in CAD and PAD. METHODS AND RESULTS: Stable CAD and PAD patients from the international, multi-centre COMPASS trial completed the visual analogue scale (VAS) of the EQ-5D-3L to assess overall health status, and the Digit Symbol Substitution test (DSST) to assess cognitive function. Main outcomes were incident development of major adverse cardiovascular events, and the combined endpoint major adverse cardiovascular or limb events. The EQ VAS (per 10 unit increase) and DSST (per 5 unit increase) were added to fully adjusted (medications, demographics, cardiovascular history and risk factors) hierarchical Cox regression models. A total of 23 433 patients were in the CAD cohort and 6899 in the PAD cohort. Among both the CAD and PAD groups, higher scores on the EQ VAS (CAD: HR = 0.89, 95%CI 0.88-0.89; PAD HR = 0.89, 95%CI 0.88-0.89) and DSST (CAD HR = 0.95, 95%CI 0.94-0.95) (PAD HR = 0.95, 95%CI 0.94-0.95) were associated with a lower risk of a major adverse cardiovascular or limb events. Population attributable risks associated with the lower two quartiles vs. upper quartiles for the EQ-5D and DSST scores were 7% and 16%, respectively in the CAD cohort; and for PAD, at 14% and 18%, respectively. CONCLUSIONS: Adding health status and cognitive functioning information to biomedical evaluations can augment cardiovascular risk-stratification in CAD and PAD. CLINICALTRIALS.GOV IDENTIFIER: NCT01776424.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".