Work disability prevention: should we focus on high body weights or heavy physical workload?
Bibliographic record
Abstract
The paper published by Robroek et al 1 estimates the incidence of work disability attributed separately and jointly to obesity and to high physical job demands in a large longitudinal cohort of Swedish construction workers. This cohort has been well described previously and has important strengths, including a large representative sample of male workers in this sector. For construction workers who participated in a programme offering periodic health examinations, height and weight, measured directly, were obtained from a first examination. The mean age of the 328 743 men at the time of first examination was 32 years of age. Approximately 29% were overweight (body mass index (BMI) 25–29) and 4% were obese (BMI >30). Physical workload measures were imputed from a job exposure matrix. Information on self-reported exposures of the frequency of lifting heavy loads and the frequency of working in bent or twisted working postures was obtained from 77 000 construction workers over the period 1989–1992. From this information, 22 categories of construction occupations were assigned to one of three ordinal groups: low (14.7%), intermediate (28.0%) and high (57.4%) physical workload. The study found an association between overweight and obese status and the receipt of a disability benefit over a mean follow-up of 22 years. (HR overweight: 1.21, 95% CI 1.19 to 1.23, HR obese: 1.70, 95% CI 1.65 to 1.76. Workers in occupations with higher …
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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 | 0.017 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.023 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".