Stressful life events, perceived degree of stress, and the risk of lung cancer
Bibliographic record
Abstract
Objective: We examined the experience of acute stressful life events and their perceived impact in relation to lung cancer incidence. Methods: In a population based case-control study in Montreal, Canada, 1061 cases and 1422 controls (recruited 1996-2001) were queried on their experience of loss events (death of a family member, divorce/separation) and socioeconomic events (job loss, major income reduction, or a move to a new city) in the prior 6 years, and their own perceived stressfulness (impact) of the event. Using multivariable unconditional logistic regression, we estimated odds ratios (ORs) and 95 % confidence intervals (CIs) of lung cancer associated with experiencing at least one of the five individual events ("any stressful event"), "any loss event" and "any socioeconomic event", and their impact. Results: Experiencing "any stressful life event" in the previous 6 years was not associated with lung cancer, even when accounting for perceived impact. When considering event type, loss events suggestively increased lung cancer odds, particularly when perceived as high impact (OR = 1.84, 95 % CI: 0.97-3.49). Socioeconomic events did not increase lung cancer odds. Conclusion: This study of recent stressful life events and their perceived impact contributes to knowledge on the possible role of psychosocial stress in lung cancer.
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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 | 0.000 | 0.002 |
| 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) | 0.001 | 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".