Factors associated to mammography exam according to two criteria
Bibliographic record
Abstract
Aims: we aimed to compare the prevalence and factors associated to mammography exam according to two Brazilian criteria.Methods: a cross-sectional, population-based study, with women between 40 and 74 years old from Rio Grande, RS, Brazil. The coverage of mammography was evaluated: 1) annual screening for women aged 40 to 74 years old; 2) biennial screening for women aged 50 to 69 years old. Data analyses were performed by Poisson regression.Results: it was included 413 (criterion 1) and 246 (criterion 2) women. The mammography coverage by criterion 1 was 49.4% (95%CI 43.8 to 55.0), ranging from a quarter for those who did not visit a doctor in the last year to two thirds for those with higher education level. Considering the criterion 2, the coverage was 65.5% (CI95% 59.2 to 71.7), ranging from one-third among those who did not visit a doctor in the last year to three-quarters among obese women.Conclusions: the mammography coverage differed according to the criterion considered. Higher socioeconomic status and having visiting a doctor in the last year were the most associated factors, regardless of the criterion.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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".