[Content of official addressed to women informative documents about breast cancer screening in Spain].
Bibliographic record
Abstract
OBJECTIVE: There are several methods to promote informed decision making before undergoing a screening program. This research aimed to analyze the contents of official documents about breast cancer screening programs. METHODS: A descriptive research was performed. After a literature review an agreed checklist was performed with the information needed to make decisions about participation in mammography screening programs. Informative documents about mammography screening valid in Spain in 2016 were analyzed by two independent researchers. The inter-rater agreement was verified and the discrepancies were solved by consensus. Absolute and relative frequencies of each item were calculated. RESULTS: 8 invitations and 14 citation letters, 12 leaflets, 8 brochures and 14 websites, from 18 screening programs, were reviewed. The information turned out to be very different according to each program. Only a third warned that participation is voluntary. 8 programs (44.4%) offered information on what is breast cancer and 7 (38.9%) on the cumulative risk of developing the disease. 15 (83.3%) explained the objectives of the program and 14 (77.8%) explained what mammography is. 14 programs (77.8%) presented as screening benefits the least invasive treatments, 12 the increase in survival (66.7%) and 10 the decrease in specific mortality (55.6%). Most of the programs did not report the possibility of false positives (27.8%) or false negatives (38.9%). Only 7 (38.9%) mentioned the possibility of overdiagnosis and 6 (33.3%) of overtreatment. CONCLUSIONS: The information provided by the different breast cancer screening programs is variable and does not contain sufficient information for informed decision-making.
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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 | 0.010 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".