Supporting Women’s BIA-ALCL Decision-making: Role of the Individual Consult in Empowering the Patient–Physician Team
Why this work is in the frame
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Bibliographic record
Abstract
Background: Breast implant associated anaplastic large cell lymphoma (BIA-ALCL) is a T-cell non-Hodgkin’s lymphoma and an uncommon risk of textured breast implants. Over the past decade, concern about BIA-ALCL has been increasing among both patients and surgeons. Patients are seeking a better understanding of their BIA-ALCL risk toward identifying a personalized care plan. This quality improvement project examines the value added by pairing group-based patient education seminars with one-on-one consults. Methods: Individual consults were held following educational group seminars. Consult field notes underwent qualitative thematic analysis. Themes were cross referenced against a quantitative chart review of patient BIA-ALCL prophylaxis decisions over time. Results: Four key themes were identified: weighing, perceiving, guiding, and supporting. Weighing considers the risk-benefit assessments patients make when weighing their BIA-ALCL risk. Perceiving describes the underlying psychosocial factors that frame patient perceptions of BIA-ALCL risk. Guiding presents the levels of guidance that patients require when making BIA-ALCL prophylaxis decisions. Supporting explores the therapeutic value of the individual consult. Ultimately, 41% of post-seminar consult attendees sought explantation, compared with 4% among patients who did not participate in this program ( P < 0.001). Conclusions: Key lessons include the following: (1) patients weigh BIA-ALCL risk against perceived surgical risks and the value of their reconstruction; (2) patients can benefit from a personalized balance of autonomy and surgeon guidance when selecting a BIA-ALCL prevention plan; (3) surgeons should seek to understand the psychosocial factors that may underlie patient perceptions of BIA-ALCL risk; and (4) individual consults can be therapeutic and help strengthen the patient–surgeon relationship.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 it