Evidence of a paradoxical relationship between endotoxin and lung cancer after accounting for left truncation in a study of Chinese female textile workers
Bibliographic record
Abstract
INTRODUCTION: Occupational exposure to endotoxin, found in Gram-negative bacteria in organic material, has been associated predominantly with a reduced risk of lung cancer among workers. An inverse exposure-response gradient among women textile workers in Shanghai, China, has been reported previously. In this case-cohort study, we investigated the influence of left truncation, which can itself induce a downward trend, on the observed association. METHODS: Subjects were enrolled between 1989 and 1991 and followed until 1998. The data were left-truncated as all subjects were hired before baseline. An analysis was performed with 3038 subcohort members and 602 cases of incident lung cancer. To evaluate left truncation, we compared lung cancer rates in those hired longer ago with those hired more recently among unexposed subjects. Cox proportional hazards modelling was used to estimate incident rate ratios (IRRs) and 95% CIs. RESULTS: Among those who were never exposed to workplace endotoxin, we compared lung cancer rates in those hired >35 years before enrolment with workers hired ≤35 years before enrolment and observed a reduced risk in the former group, IRR=0.74, 95% CI (0.51 to 1.07). After accounting for this downward bias from left truncation, the reduced risk associated with endotoxin remained among those hired ≤50 years before enrolment. In contrast, there was suggestion of an increased risk of lung cancer among those hired >50 years ago. CONCLUSIONS: After examination of left truncation bias, an inverse dose-response between endotoxin and lung cancer remained for all subjects except those hired longest ago.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".