Healthy worker effect in cohort studies on chronic bronchitis
Bibliographic record
Abstract
OBJECTIVES: Despite the recognition of selection biases arising from the healthy worker effect in occupational mortality studies, the possibility of similar effects in occupational cohort studies on respiratory symptoms is not well known. Two mechanisms are responsible for the healthy worker effect in respiratory cohort studies. One is health-based selection of workers into employment (healthy him effect), and the other is health-based differential losses to follow-up (healthy worker survivor effect). The aim of the present paper was to estimate the magnitude of the healthy worker survivor effect in cohort studies of symptoms of chronic bronchitis. METHODS: A meta-analysis of occupational cohort studies of symptoms of chronic bronchitis was performed that included published articles identified in searches of the Medline bibliographic databases between 1980 and March 2001 and the reference lists of the located articles. RESULTS: Eight cohort studies were identified using an a priori selection criterion. The pooled odds ratio of the prevalence of chronic bronchitis for subjects leaving the cohorts was 1.23 when these subjects were compared with those who remained under study (95% confidence interval 1.04-1A4). CONCLUSIONS: The prevalence of respiratory symptoms among exposed workers may he underestimated if the healthy worker survivor effect is not taken into account
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.106 | 0.224 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.012 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| 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".