Risk factors for incident cardiovascular events and their population attributable fractions in rural India: The <scp>Rishi Valley Prospective Cohort Study</scp>
Bibliographic record
Abstract
OBJECTIVE: We prospectively determined incident cardiovascular events and their association with risk factors in rural India. METHODS: We followed up with 7935 adults from the Rishi Valley Prospective Cohort Study to identify incident cardiovascular events. Using Cox proportional hazards regression, we estimated hazard ratios (HRs) with 95% confidence intervals (95% CI) for associations between potential risk factors and cardiovascular events. Population attributable fractions (PAFs) for risk factors were estimated using R ('averisk' package). RESULTS: Of the 4809 participants without prior cardiovascular disease, 57.7% were women and baseline mean age was 45.3 years. At follow-up (median of 4.9 years, 23,180 person-years [PYs]), 202 participants developed cardiovascular events, equating to an incidence of 8.7 cardiovascular events/1000 PYs. Incidence was greater in those with hypertension (hazard ratio [HR] [95% CI] 1.73 [1.21-2.49], adjusted PAF 18%), diabetes (1.96 [1.15-3.36], 4%) or central obesity (1.77 [1.23, 2.54], 9%) which together accounted for 31% of the PAF. Non-traditional risk factors such as night sleeping hours and number of children accounted for 16% of the PAF. CONCLUSIONS: Both traditional and non-traditional cardiovascular risk factors are important contributors to incident cardiovascular events in rural India. Interventions targeted to these factors could assist in reducing the incidence of cardiovascular events.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".