Delivering Breast Reconstruction Information to Patients—Part 2: Women Report on Preferred Information Content
Bibliographic record
Abstract
PURPOSE: To determine the type of information women want to be provided in order to make an informed decision as to whether, when, and using what technique to proceed with breast reconstruction. METHOD: Using purposeful sampling, 19 patients who had recently undergone various breast reconstruction procedures were recruited to each participate in a 30- to 45-minute semi-structured interview. Participants shared their insights and beliefs regarding the type of breast reconstruction information they most valued prior to undergoing breast reconstruction surgery. Participants were also queried as to perceived information gaps. In some cases, the participants' partners or support persons were also interviewed. Grounded theory and thematic analysis assisted in interview transcript analysis. RESULTS: Eight topics were identified relating to women's informational needs around breast reconstruction. Examples include how to weigh the pros and cons of various breast reconstruction options to decide between flap or implant reconstruction, whether there are safety concerns with immediate breast reconstruction or nipple-sparing reconstruction, and expectations and advice on how to manage possibly unexpected intimacy issues after breast reconstruction. CONCLUSIONS: Using mixed methods research methodology, 19 women reported on preoperative informational gaps relating to their recent breast reconstruction experiences. Patients report that adequate breast reconstruction information prior to breast reconstruction surgery helps them to manage their expectations, prepare for surgery and recovery, and improve postoperative satisfaction.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".