Enduring Records: The Environmental and Cultural Heritage of Wetlands BARBARA PURDEY (Ed.)302 pp., 195 b&w figuresOxbow Books, Park End Place, Oxford OX1 1HN, or, David Brown Book Co., POB 511, Oakville, CT 06779, 2001, £48 or $85 (hbk), ISBN 1-84217-0488-1
Bibliographic record
Abstract
Occupational exposures related to military service may increase the risk of cancer for military Veterans, while high levels of fitness during service may decrease risk. However, few studies have compared this post-career cancer risk directly to the employed general population.This retrospective cohort study used linked administrative data. Canadian Armed Forces and Royal Canadian Mounted Police Veterans in Ontario, Canada were matched 1:4 on age, sex, geography, and community-level income to a group of non-Veterans most likely to have been employed during a period similar to the Veterans’ military service. Cancer diagnoses were identified using the Ontario Cancer Registry.During the study period, 642 of 30 576 included Veterans (2.1%) and 3408 of the 122 293 matched general population cohort (2.8%) experienced at least one cancer diagnosis. The crude rate of cancer was 153.5 per 100 000 person-years among Veterans vs. 205.9 per 100 000 person-years for the general population cohort. After adjusting for rurality and matching variables, Veterans had an 27% lower risk of developing any cancer than their matched comparators [hazard ratio = 0.73 (95% CI: 0.67–0.80)]. Among specific cancer types, the risk of lung and colorectal cancer was significantly lower for Veterans relative to the general population cohort; the risk of breast and prostate cancer was similar.This study adds to the growing international evidence suggesting that risk of many cancers among Veterans is lower or similar to the general population. Further understanding of the complex relationships among occupational exposures, environmental factors, and lifestyle factors is needed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.044 | 0.034 |
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".