Supporting Women’s BIA-ALCL Decision-making: Role of the Individual Consult in Empowering the Patient–Physician Team
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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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 | 0.018 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".