Towards population-based genetic screenings for breast and ovarian cancer: A comprehensive review from economic evaluations to patient perspectives
Bibliographic record
Abstract
Genetic testing for hereditary breast and ovarian cancer following genetic counseling is based on guidelines that take into account particular features of the personal and family history, and clinical criteria conferring a probability of having a BRCA mutation greater than 10% as a threshold for accessing the test. However, besides reducing mortality and social impact, the extension of screening programs also for healthy family members would allow a huge saving of the rising costs associated with these pathologies, supporting the choice of the "Test" strategy versus a "No Test" one. Analyses of different health care systems show that by applying the "Test" strategy on patients and their families, a decrease in breast and ovarian cancer cases is achieved, as well as a substantial decrease in costs of economic resources, including the costs of the clinical management of early detected tumors. In this review, we analyzed the most recent papers published on this topic and we summarized the findings on the economic evaluations related to breast and ovarian cancer population screenings. These results proved and validated that the population-wide testing approach is a more accurate screening and preventive intervention than traditional guidelines based on personal/family history and clinical criteria to reduce breast and ovarian cancer risk.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".