Risk factors for ischemic heart disease in professional drivers: a meta-analysis
Bibliographic record
Abstract
Summary. Objective. To identify risk factors for ischemic heart disease (IHD) in professional drivers through a systematic review and meta-analysis. Methods. A comprehensive literature search was conducted across multiple databases, including CNKI, CBM, Wanfang Data, VIP, FMRS Medline, The Cochrane Library, PubMed, Embase, and Web of Science, for studies published from January 1, 1990, to December 31, 2024. Keywords such as “ischemic heart disease,” “coronary heart disease,” “myocardial infarction,” “driver,” and “risk factor” were used. Relevant case-control studies were included based on predefined criteria and quality assessed using the Newcastle-Ottawa Scale (NOS), with studies scoring ≥7 considered high quality for meta-analysis. Results. Eleven studies involving 95,791 cases and 29,621 controls were included. The meta-analysis revealed a significant association between the driving profession and an increased risk of IHD (OR = 1.85, 95% CI: 1.63-2.11). Drivers with hypertension (OR = 2.58), smoking (OR = 2.70), obesity (OR = 1.54), diabetes (OR = 1.77), physical inactivity (OR = 2.12), dyslipidemia (OR = 1.82), and occupational stress (OR = 2.31) all exhibited significantly higher risks of IHD. Conclusions. The driving profession is a significant risk factor for IHD. Drivers with chronic conditions, unhealthy lifestyles, dyslipidemia, and occupational stress face an elevated risk. Health interventions should focus on lifestyle changes, managing hypertension and diabetes, and reducing work-related stress.
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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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.057 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".