Non-Performance of Cancer Screening in Peru: A Comparative Analysis between Regions Exposed and Unexposed to Ozone Layer Mini-Hole
Bibliographic record
Abstract
Objective: To determine the prevalence and factors associated with non-performance of cancer screening in Peru based on an analysis between a department exposed to an ozone layer mini-hole versus an unexposed one. Materials and Methods: Analytical cross-sectional study. The study included individuals aged 15 years and older who responded to questions about cancer screening in the Demographic and Family Health Surveys 2017-2022, comparing a department exposed to an ozone layer mini-hole (Arequipa) with an unexposed one (Lima). Sociodemographic, geographic, health status, and cancer knowledge variables were obtained. Multivariate analysis was performed using Poisson regression with robust standard error. Results: The study included 10,546 people. The prevalence of non-performance of cancer screening was 75.2%. Multivariate analysis revealed that male sex was a significant risk factor (aPR=1.41; 95% CI: 1.37-1.44), while access to health insurance (aPR=0.91; 95% CI: 0.89-0.93) and the belief that cancer is preventable (aPR=0.92; 95% CI: 0.88-0.95) were protective factors. Education level and wealth index also showed associations with aPR values close to 1. Residence in Arequipa was not a significant factor for non-participation in cancer screening. Conclusion: The prevalence of non-performance of cancer screening was high. The main factor associated with not undergoing cancer screening was male sex, while having access to health insurance and believing that cancer is preventable were protective factors.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".