Screening mammography beyond the limit recommended in a Portuguese Primary Care Centre: a cross-sectional study
Bibliographic record
Abstract
Background.Despite the imminent change and controversy around screening mammography, many women are still screened beyond the recommended age.Objectives.This study aims to characterise these women and to identify factors associated with this decision. Material and methods.A cross-sectional study including women who attended a Portuguese primary health care centre and who performed at least one mammography after turning 70 from March 2007 until July 2019.Data was collected by accessing the electronic health records.Results.Among all women who underwent mammography after the age of 50, 5.5% were 70 years of age and older.The main risk factor for breast cancer (BC) identified was the presence of other breast abnormalities (46.3%).Most requests in the screening group were for women under 75 years of age (79.2%) and were performed by the family physician (76.9%).Adherence to the BC national screening programme was lower in the screening group (73.3% vs 84.8%).After logistic regression, age at the time of request (OR = 0.815, 95% CI: 0.720-0.922,p = 0.001) and initiative of the request (OR = 0.176, 95% CI: 0.044-0.707,p = 0.014) of the last mammography added significantly to the model.Conclusions.Only a small proportion of screening mammographs were performed on women beyond the recommended age limit, which complied with the National Cancer Plan currently implemented in Portugal.Patients' age, presence of arterial hypertension or osteoporosis or hip fracture, number of comorbidities and the initiative of the request may have contributed to the decision to continue screening.Our study provides important clues for understanding factors associated with prolonging BC screening.However, further research is still needed.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".