Shared social mechanisms underlying the risk of nine cancers: A life course study
Bibliographic record
Abstract
Identifying life periods during which social conditions have the highest impact on risk of common cancers in a population may help to reveal their underlying shared social mechanisms. We used the life course framework to estimate the extent to which life course SEP is associated with risk of nine cancers. In addition, we tested whether these associations conform to a critical period or cumulative life course model. Data were from a population-based case-control study of occupational exposures and cancer conducted in Montreal, Canada. Participants were males aged 35-70 years (n = 2,547) residing in the Montreal metropolitan area with primary, histologically confirmed cancers diagnosed between 1979 and 1985. Population controls (n = 512) were sampled from electoral lists. SEP was measured at three different periods of life based on respondent's report: during childhood, young adulthood and mid-life. We used a structured modeling approach using a series of unconditional logistic regressions to test which models best fit the data. Life course SEP increased the risk of all cancers. SEP in childhood was identified as a critical period for prostate and all gastrointestinal tract cancers except for esophagus cancer. In addition, the accumulation model best explained the data for melanoma and lung squamous cell carcinoma. Our findings suggest that childhood social circumstances are a common risk factor for several cancers among men; our results provide insights into the mechanisms involved in the etiology of nine cancers.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".