Cancer in General Responders Participating in World Trade Center Health Programs, 2003–2013
Bibliographic record
Abstract
BACKGROUND: Following the September 11, 2001, attacks on the World Trade Center (WTC), thousands of workers were exposed to an array of toxins known to cause adverse health effects, including cancer. This study evaluates cancer incidence in the WTC Health Program General Responder Cohort occurring within 12 years post exposure. METHODS: The study population consisted of 28 729 members of the General Responder Cohort enrolled from cohort inception, July 2002 to December 31, 2013. Standardized incidence ratios (SIRs) were calculated with cancer case inclusion and follow-up starting post September 11, 2001 (unrestricted) and, alternatively, to account for selection bias, with case inclusion and follow-up starting 6 months after enrollment in the WTC Health Program (restricted). Case ascertainment was based on linkage with six state cancer registries. Under the restricted criterion, hazard ratios were estimated using multivariable Cox proportional hazards models for all cancer sites combined and for prostate cancer. RESULTS: Restricted analyses identified 1072 cancers in 999 responders, with elevations in cancer incidence for all cancer sites combined (SIR = 1.09, 95% confidence interval [CI] = 1.02 to 1.16), prostate cancer (SIR = 1.25, 95% CI = 1.11 to 1.40), thyroid cancer (SIR = 2.19, 95% CI = 1.71 to 2.75), and leukemia (SIR = 1.41, 95% CI = 1.01 to 1.92). Cancer incidence was not associated with any WTC exposure index (composite or individual) for all cancer sites combined or for prostate cancer. CONCLUSION: Our analyses show statistically significant elevations in cancer incidence for all cancer sites combined and for prostate and thyroid cancers and leukemia. Multivariable analyses show no association with magnitude or type of exposure.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".