Assisting with Decision-Making: How Standardized Information Impacts Breast Cancer Patient Decisions Regarding Fertility Trade-Offs and Chemotherapy
Bibliographic record
Abstract
Purpose: Fertility is a concern for young women with breast cancer. We explore patient preferences for chemotherapy and whether women will trade-off survival benefits to maintain fertility following standardized information delivery. Methods: During a standardized interview, outcomes associated with adjuvant chemotherapy and 5 years of tamoxifen (CT) or 5 years of tamoxifen alone (NoCT) were described to participants. A threshold task was performed, in which each participant participated in two scenarios: (1) 10% absolute survival benefit from treatment and (2) 25% absolute survival benefit from treatment. The threshold point represented the reduction in fertility post-treatment that a participant would accept before she would trade-off CT benefit. Descriptive statistics were used to characterize participants. Demographic factors (age, marital status, parity at diagnosis, and education) associated with willingness to trade-off survival benefits were evaluated with logistic regression. Results: Analysis comprised 50 women with a median age of 34.5 years (range 25–39 years). Thirty-nine women (78%) completed university education. Thirty-four (68%) and 45 (90%) women in scenarios 1 and 2, respectively, were willing to trade-off all fertility (i.e., reduce fertility to 0% chance of conceiving naturally) to undertake CT and maintain survival benefits. Eight (16%) and three (6%) women in scenarios 1 and 2, respectively, chose to not pursue CT at all to maintain natural fertility. Regression analysis did not identify any variables that were predictive of participants' preferences. Conclusion: Most women with breast cancer are not willing to trade-off survival benefits of adjuvant therapy to maintain fertility.
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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 | 0.008 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".