Professional driver mortality in 9 countries: a systematic review and meta-analysis
Bibliographic record
Abstract
A systematic review, combining analysis (on means after deleting outliers from samples) and meta-analysis for Standardized mortality ratio (SMR) versus the general population for total and all-cancer mortality for professional drivers (men only) of various countries (cohorts of Great Britain, Denmark, Iceland, Italy, Canada, Russia, Singapore, Switzerland and Sweden (1988–2002); data for the USA (1978) were not available; 13 papers in total) were conducted. The criteria for the search and selection of sources (PubMed, Google, Cochrane Systematic Reviews and reference lists of publications) were: a) a cohort of drivers only, without adding other employees of auto enterprises; b) the presence in the study of index of total mortality (‘all causes’) and/or mortality from all malignant neoplasms (‘all cancer’); c) the expression of mortality rates only in the SMR index. A combined analysis and meta-analysis showed a weak healthy worker effect (HWE; meta-analysis: SMR=0.92, 95% confidence intervals (CI): 0.85, 0.99, the presence of HWE was judged by the value of the upper CI<1.0). There are also trends towards HWE for overall mortality for taxi and truck drivers (SMR=0.9–0.93), but the samples are too small to draw conclusions. For other groups of drivers HWE was not found. In both types of synthetic studies, no SMR for HWE was observed for mortality from all malignancies, either for the general group of drivers (8 countries; 16 cohorts) or for individual occupational groups. There were slight increases in SMR (by 3–10%), the magnitude of which, according to epidemiological canons, is difficult to prove, and according to the risk scale by R.R. Monson corresponds to no effect. The absence of significant risks both in terms of the integral indicator of well-being (life expectancy, inversely proportional to SMR), and in terms of mortality from all types of malignant neoplasms for professional drivers makes it unlikely that the population of Russia will experience increased mortality due to ever-increasing mass motorization.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.037 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".