Consideraciones para comunicar riesgos y beneficios de la mamografía a mujeres desde la perspectiva de los expertos
Bibliographic record
Abstract
BACKGROUND: Breast cancer (BC) has a high mortality rate in developing countries due to a scarcity of early detection. Risk communication is critical to support women who face the decision to undertake BC screening. Thus, they can balance their perceived and real risk, and make informed choices. AIM: To describe experts' views on how the provision of information related to BC screening should be made. MATERIAL AND METHODS: A qualitative study with focus groups with national experts was conducted. Open coding was performed. RESULTS: Four categories on the way information about BC screening should be provided emerged: to communicate about the need of the exam; the pros and cons of the test; fear as a barrier for understanding; and involving women in the decision-making process. CONCLUSIONS: These findings emphasize the need to include risk communication strategies in the patient-provider relationship and encourage and respect women's autonomy when facing the BC screening decision.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 teacher head, 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".