Cross‐shift peak expiratory flow changes are unassociated with respirable coal dust exposure among South African coal miners
Bibliographic record
Abstract
BACKGROUND: The objectives of this study were to determine whether cross-shift changes in peak expiratory flow rate (PEFR) were related to respirable dust exposure in South African coalminers. METHODS: Fifty workers were randomly selected from a cohort of 684 miners from 3 bituminous coalmines in Mpumalanga, South Africa. Peak expiratory efforts were measured prior to the commencement of the shift, and at the end of the shift on at least two occasions separated by at least 2 weeks, with full shift personal dust sampling being conducted on each occasion for each participant. Interviews were conducted, work histories were obtained and cumulative exposure estimates were constructed. Regression models examined the associations of cross-shift changes in PEFR with current and cumulative exposure, controlling for shift, smoking and past history of tuberculosis. RESULTS: There were marginal differences in cross-shift PEFR (ranging from 0.1 to 2 L/min). Linear regression analyses showed no association between cross-shift change in PEFR and current or cumulative exposure. The specific shift worked by participants in the study showed no effect. CONCLUSIONS: Our study showed no association between current respirable dust exposure and cross-shift changes in PEFR. There was a non-significant protective effect of cumulative dust exposure on the outcome, suggesting the presence of a "healthy worker survivor effect" in this data.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".