Controlling for Potential Confounding by Occupational Exposures
Bibliographic record
Abstract
Occupational exposure is an important potential confounder in air pollution studies because it is plausible that individuals who live in highly polluted areas also work in more polluted environments. While the original investigators made some efforts to control for possible confounding by occupational variables, it was felt that these could be improved upon. The reanalysis team attempted to control for occupational confounding by supplementing the original data sets with two new variables, an indicator of the "dirtiness" of a subject's job and an indicator of possible exposure to occupational lung carcinogens. The attribution of these variables was based on the job title recorded by the original investigators and on the judgment of our experts concerning typical exposure patterns in different occupations. We fitted Cox proportional-hazards models identical to those that had been used by the original investigators while also including one or both of the new occupational covariates in the models. In none of the analyses did the inclusion of the occupational variables materially change the results. It would therefore appear that, in general, the results reported by the original investigators were not distorted by inadequate control of occupational variables. We also carried out some analyses using the dirtiness index as a stratification variable to assess effect modification. There was some indication, albeit inconsistent, that the effect of air pollution on mortality was greater among subjects with dirty jobs than among those with clean jobs.
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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 | 0.055 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".