Shared decision-making: Applying a person-centered approach to tailored breast reconstruction information provides high satisfaction across a variety of breast reconstruction options
Bibliographic record
Abstract
BACKGROUND: A person-centered approach to co-decision-making using tailored information respects each woman's preferences and may heighten breast reconstruction satisfaction. METHODS: Women seeking breast reconstruction underwent initial and follow-up consultations wherein suitable options were discussed, and take-away material, balanced website links, and access to a nurse specialist and peer volunteers was provided. After reconstruction, the BRECON-31(©) was administered and analyzed in three groups: autologous, alloplastic, and latissimus dorsi (LD)/implant. BRECON-31(©) subscale scores were compared between the groups, and multiple regression used to determine if the type of reconstruction independently predicted satisfaction. RESULTS: One hundred twenty three of 176 (70%) women completed the questionnaire (43% autologous, 47% alloplastic, and 10% LD/implant reconstructions). The LD/implant group had a low rate of immediate reconstruction (8.3%, P = 0.04), and the highest rate of chemotherapy (91.7%, P = 0.002) and radiation (100%, P = 0.003). The alloplastic group had a high rate of bilateral reconstruction (86.8%, P = 0.01). All groups scored well on the self-image, arm concerns, intimacy, satisfaction, and expectations subscales. All groups scored moderately on the self-consciousness, appearance, and nipple subscales. The autologous group scored the lowest on recovery (51 vs. 68 and 65, P < 0.0001) and only moderately well on the abdomen subscale (67). Multiple regression analysis showed that satisfaction was not driven by type of reconstruction (P > 0.05). CONCLUSION: High satisfaction can be achieved using a person-centered approach by providing detailed information, appreciating each woman's unique features, and tailoring the reconstruction plan to the individual. Recovery remains a particular challenge, especially for women undergoing autologous reconstruction.
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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 | 0.001 | 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 | 0.001 |
| 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".