CRITICAL WINDOWS: HOW TIME PATTERNS OF EXPOSURE CAN INFLUENCE DISEASE RISK
Bibliographic record
Abstract
ISEE-144 Introduction: In environmental and occupational settings, carcinogenic exposures are often repeated or protracted over time. The time pattern of exposure accrual may influence subsequent temporal patterns of cancer risk. Several simple models that may be used for temporal variation in exposure effects are presented. Methods: A cohort of 40,415 nuclear industry workers was identified via the Canadian National Dose Registry. Vital status and cause of death were ascertained through 1994. Associations between ionizing radiation and mortality due to lung cancer and leukaemia were quantified using conditional logistic regression models with risk sets constructed by incidence density sampling. A step function, a bilinear function, and a sigmoid function were used to evaluate variation in exposure effects with time-since-exposure and age-at-exposure. Results: Step and sigmoid functions were used to explore latency and morbidity periods. For analyses of lung cancer and leukaemia the best fitting models were obtained when exposure assignment was lagged by 13 and 0 years, respectively. A bilinear function was used to evaluate whether exposure effects diminished with time-since-exposure. There was evidence of attenuation of radiation effects with protracted time-since-exposure. In analyses of age at exposure, there was evidence of variation in radiation-mortality associations; discounting radiation doses accrued at younger ages (e.g. 15-35 years) led to significant improvements in model fit. Conclusions: This paper illustrates empirical approaches to evaluating temporal variation in the effect of a protracted exposure on disease risk.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".