Impact of a Medical Fitness Model on Incident Major Adverse Cardiovascular Events: A Prospective Cohort Study of 11 000 Members
Bibliographic record
Abstract
BACKGROUND: Cardiovascular disease remains the leading cause of disease burden and death in the world. The medical fitness model may be an alternative public health strategy to address cardiovascular risk factors with medical integrated programming. METHODS AND RESULTS: We performed a retrospective cohort study between January 1, 2005, and December 31, 2015. Adults (aged ≥18 years) who did not have a prior major adverse cardiovascular event were included. Controls were assigned a pseudo-index date at random on the basis of the frequency distribution of start dates in the medical fitness facility group. Multivariate Cox proportional hazards regression models were adjusted for age, sex, socioeconomic status, comorbidities, and year of index date. We stratified the medical fitness facility group into low-frequency attenders (≤1 weekly visit) and regular-frequency attenders (>1 weekly visit). Our primary outcome was a hospitalization for nonfatal myocardial infarction and stroke, heart failure, or cardiovascular death. We included 11 319 medical fitness facility members and 507 400 controls in our study. Compared with controls, members had a lower hazard risk of a major adverse cardiovascular event-plus (hazard ratio [HR], 0.88 [95% CI, 0.81-0.96]). Higher weekly attendance was associated with a lower hazard risk of a major adverse cardiovascular event-plus compared with controls, but the effect was not significant for lower weekly attendance (low-frequency attenders: HR, 0.94 [95% CI, 0.85-1.04]; regular-frequency attenders: HR, 0.77 [95% CI, 0.67-0.89]). CONCLUSIONS: Medical fitness facility membership and attendance at least once per week may lower the risk of a major adverse cardiovascular event-plus. The medical fitness model should be considered as a public health intervention, especially for individuals at risk for cardiovascular disease.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".