Individualization of Decision Making Regarding Mammographic Screening for Breast Cancer in Women 40-49 y.o. with First Degree Relative with Breast Cancer
Bibliographic record
Abstract
This paper aims to help health care providers to advise the healthy female patients age of 40-49 y.o. who have one first degree relative with breast cancer, whether she should or should not participate in mammographic screening. The author’s patient is anxious whether she should have her mammogram done and whether it will benefit her. Her sister was most recently diagnosed with breast fibroadenoma. In order to answer their patient’s question the author take into consideration medical aspects of mammography as a screening test, and integrate them with patient values and preferences into a single decision-making model. This paper is based on the modeling of a decision tree, using the information extracted from open sources and peer-reviewed publications. It is based on comprehensive search for each model parameter, but is not an all-inclusive systematic review. The purpose of this work is both educational and practical: the authors try to apply the decision analysis methodology in an attempt to solve this dilemma while trying to avoid or at least minimize biased assumptions regarding usefulness of mammography in this group of patients. Based on their proposed model the decision regarding the participation into mammographic screening in this particular scenario is highly driven by patient values and preferences.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".