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 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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".