P5-15-03: Development of a Patient Decision Aid for Women 70 Years and Older with Stage I, Hormonally Sensitive, Breast Cancer Considering Adjuvant Treatment Post-Lumpectomy.
Bibliographic record
Abstract
Abstract Background: Decision Aids (DA) are developed with the intent to support people in making specific and deliberate choices by improving information transfer about different outcomes. Previous research has shown that DAs can increase patient knowledge regarding treatment options, reduce decisional conflict, and increase patient satisfaction with the decision-making process. However, no DAs have been developed to help older breast cancer patients decide whether or not to undergo adjuvant RT. We developed and tested a DA for older women with stage I,ER/PR positive breast cancer considering adjuvant treatment post-lumpectomy and we examined its impact on treatment decision-making process. Methods and Materials: A DA was developed and evaluated in three steps following the Ottawa Decision Aid Framework: 1) Needs assessment (N=16); 2) Pilot I, to examine the DA's acceptability (N=12); and 3) Pilot II, a pre-test post-test (N=38) with older women with ER/PR responsive breast cancer post-lumpectomy who were receiving adjuvant RT. Measures included questionnaires to assess patient's satisfaction with the DA, patients’ self-reported decisional conflict (DC), level of distress, treatment-related knowledge, and choice predisposition Results: The DA is a booklet that details each adjuvant treatment option's benefits, risks and side-effects tailored to their clinical profile; includes a value clarification exercise; and steps to guide them towards their own treatment decision. All women felt the DA was helpful and informative. Compared with baseline scores, patients had a statistically significant (p < .05) reduction in DC (adjusted mean difference [AMD], −7.18; 95% confidence interval [CI], −13.50 to 12.59); increased clarity of the treatment benefits and risks (AMD, −10.86, CI, −20.33 to 21.49; and improved general treatment knowledge (AMD, 8.99, CI, 2.88 to 10.28) after using the DA. General trends were also reported in patient's choice predisposition scores suggesting potential differences in treatment decision after DA use. Discussion: This study provides evidence that this DA may be a helpful educational tool for this group of women. The quality of care for older breast cancer patients may be enhanced by using a tailored DA to help the patient be informed of their treatment options and to prepare for decision-making. Citation Information: Cancer Res 2011;71(24 Suppl):Abstract nr P5-15-03.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".