Night shift work and stomach cancer risk in the MCC-Spain study
Bibliographic record
Abstract
OBJECTIVES: Night shift work has been classified as a probable human carcinogen by the International Agency for Research on Cancer, based on experimental studies and limited evidence on human breast cancer risk. Evidence at other cancer sites is scarce. We evaluated the association between night shift work and stomach cancer risk in a population-based case-control study. METHODS: A total of 374 incident stomach adenocarcinoma cases and 2481 population controls were included from the MCC-Spain study. Detailed data on lifetime night shift work were collected including permanent and rotating shifts, and their cumulative duration (years). Adjusted unconditional logistic regression models were used in analysis. RESULTS: A total of 25.7% of cases and 22.5% of controls reported ever being a night shift worker. There was a weak positive, non-significant association between ever having had worked for at least 1 year in permanent night shifts and stomach cancer risk compared to never having worked night shifts (OR=1.2, 95% CI 0.9 to 1.8). However, there was an inverse 'U' shaped relationship with cumulative duration of permanent night shifts, with the highest risk observed in the intermediate duration category (OR 10-20 years=2.0, 95% CI 1.1 to 3.6) (p for trend=0.19). There was no association with ever having had worked in rotating night shifts (OR=0.9, 95% CI 0.6 to 1.2) and no trend according to cumulative duration (p for trend=0.68). CONCLUSION: We found no clear evidence concerning an association between night shift work and stomach cancer risk.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".