UK and Canadian Gulf War Veteran Mortality: Using A Fellow Military Cohort as a Comparison Population
Bibliographic record
Abstract
IntroductionTo compare 1990-91 Gulf War Veterans (GWV) survival outcomes with a comparable cohort, UK’s Ministry of Defence and Canada’s Department of National Defence combined data from their respective cohorts. The survival estimates/comparisons emanating from this collaboration will be novel as they will control for healthy worker/soldier effect (HW/SE). Objectives and ApproachGWV cohort building and record linkage methods used by Canada and the UK are described in more detail elsewhere. To ensure comparability in mortality outcomes between cohorts, the following steps will be conducted prior to analysis: ICD-9 causes of death (COD) will be recoded to ICD-10; recoding by each country will be cross-validated by the other, to ensure high inter-coder reliability; CODs will be analysed at the ICD-chapter level; Calculated age- and sex-specific rates will be directly standardized using the WHO 2012-2022 Standard Population. Cox proportional hazards will be used to compare survival between cohorts. ResultsWe are currently in the process of completing this exciting cross-sectoral linkage study and expect to have preliminary results to present. To our knowledge, this will be the first time that mortality outcomes for two discrete Gulf War veteran cohorts (ascertained by record linkage) will be analytically compared, rather than comparing to the general population. These findings will not only provide a more recent evaluation of the health status of GWV in Canada, but will also be a rare opportunity to control for the HW/SE, using comparisons with non-equivalent cohorts (e.g., general population, other deployment) cannot achieve. Conclusion/ImplicationsBeyond evidence of a strong inter-sectoral research relationship between military nations, these findings also represent a feasible solution to controlling for the HW/SE. The ability to control for this will mean more accurate UK and Canada GWV mortality/survival estimates than either country can generate on their own.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".