Author's reply to: Air pollution and incident bladder cancer: A risk assessment
Bibliographic record
Abstract
We thank Kawada1 for comments on our recent paper.2 We agree that air pollution exposure assessment is one of the main limitations of our analysis, being based on modeled estimates of recent concentrations assigned to the participant residence at enrollment some 10 years prior. Although addressing limitations of previous work based on surrogate indicators of ambient air pollution, further work with more detailed individual-level estimates of historical exposure prior to cancer occurrence may be useful. Kawada1 also points out that our analysis employed unconditional as opposed to conditional logistic regression models to estimate associations of ambient air pollution and incident bladder cancer risk. Unconditional logistic regression analysis of matched case–control studies adjusting for matching variables results in valid and possibly more precise estimates of association compared to conditional logistic regression when sparse data are not of concern.3 Our analysis also adjusted for a range of bladder cancer risk factors in both the main analysis and in a range of sensitivity analyses with little change in findings observed. Other analyses of air pollution and bladder cancer based on either minimal or fully adjusted approaches have also noted little change in relative risk estimates observed.4 Yours sincerely Michelle C. Turner Esther Gracia-Lavedan Marta Cirac Gemma Castaño-Vinyals Núria Malats Adonina Tardon Reina Garcia-Closas Consol Serra Alfredo Carrato Rena R. Jones Nathaniel Rothman DebraT. Silverman Manolis Kogevinas MCT is funded by a Ramón y Cajal fellowship (RYC-2017-01892) from the Spanish Ministry of Science, Innovation and Universities and co-funded by the European Social Fund. ISGlobal is a member of the CERCA Programme, Generalitat de Catalunya.
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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 | 0.010 | 0.087 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.026 | 0.039 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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".