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 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 | 0.015 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".