Navigating Women’s BIA-ALCL Information Needs: Group Seminars May Offer an Opportunity to Empower the Patient–Surgeon Team
Bibliographic record
Abstract
Background: Breast Implant Associated Anaplastic Large Cell Lymphoma (BIA-ALCL) is a T-cell non-Hodgkin’s lymphoma that has been linked to textured breast implants, and is an emerging concern within the plastic and reconstructive surgery community. Many surgeons are struggling with how best to inform their patients and manage BIA-ALCL care without overwhelming their standard clinical practice. Methods: Five educational group seminars were held for 53 patients. A thematic analysis of the field notes taken at each seminar was conducted to identify recurring patient and surgeon behaviors. Results: The thematic analysis identified 5 key themes: seeking, amplifying, framing, trusting, and empowering. Seeking describes the knowledge sought by patients and their varying engagement in their care. Amplifying underlines how the emotionally charged topic of BIA-ALCL impacted patient and surgeon behaviors. Framing presents surgeon efforts to help patients understand the risk level of BIA-ALCL. Trusting addresses the ways BIA-ALCL has impacted patient trust in the medical community and the mechanisms to rebuild this trust. Empowering outlines surgeon efforts to engage patients in shared decision-making. Conclusions: Herein is presented a possible framework for efficient BIA-ALCL patient education that can be adapted to different surgical practices. Lessons learned are: (1) patients want information on BIA-ALCL’s clinical features and prophylactic implant removal; (2) BIA-ALCL discussions are emotionally charged and surgeons must remain cognizant of group dynamics and that the physician–patient power differential may impact patient decision-making; (3) patient trust has been strained but can be restored; and (4) patient responses to BIA-ALCL are variable and subjective; thus, surgeons should emphasize patient-centered care.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".