Reply to Comment on: ‘Possible pro-carcinogenic association of endotoxin on lung cancer among Shanghai women textile workers’
Bibliographic record
Abstract
We appreciate the thoughtful comments by Rylander and Jacobs (2014) on our paper ( Checkoway et al, 2014 ). The absence of an inverse exposure–response relation for endotoxin and lung cancer in the extended follow-up was somewhat unexpected in view of the reported consistent findings from numerous prior studies, including our initial follow-up of the Shanghai textile worker cohort ( Astrakianakis et al, 2007 ). Although neither the modest excess relative risks observed nor the exposure–response trend for exposures >15 years since first exposure (Table 3) were statistically significant, the findings are somewhat suggestive of a possible late pro-carcinogenic effect. We do not believe that our observations on endotoxin exposure and lung cancer risk necessarily challenge a well-established association. Instead, we would argue that the exposure–response association may change over time owing to complex, yet poorly understood, underlying mechanisms. We are also not the first to report that an inverse association between endotoxin and lung cancer risk may be time varying, diminishing over time ( Mastrangelo et al, 2005 ).
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.038 | 0.029 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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".