The Macrotextured Implant Recall: Breast Implant–Associated-Anaplastic Large Cell Lymphoma Risk Aversion in Cosmetic and Reconstructive Plastic Surgery Practices
Bibliographic record
Abstract
BACKGROUND: The recall of Allergan Biocell (Irvine, CA) devices due to the association between anaplastic large cell lymphoma (ALCL) and macrotextured breast implants means that plastic surgeons are faced with the challenge of caring for patients with these implants in situ. Cosmetic and reconstructive surgeons have been contacting affected patients to encourage them to follow up and discuss the most appropriate risk-reduction strategies. OBJECTIVES: The aim of this study was to evaluate patient concerns about the risk of breast implant-associated ALCL (BIA-ALCL) and to compare management differences between cosmetic and reconstructive patients. METHODS: A retrospective review was performed of 432 patients with macrotextured implants who presented to clinic after being contacted (121 reconstructive and 311 cosmetic). These records were analyzed for their presenting concerns, surgery wait times, and management plans. Statistical analysis was performed to compare the cohorts, and odds ratios (ORs) were computed to determine the association between patient concerns and their choice of management. RESULTS: After consultation, 59.5% of the reconstructive cohort and 49.5% of the cosmetic cohort scheduled implant removal or exchange. The reconstructive population had a higher rate of ALCL concern (62.7%); however, both cohorts had a significant OR, demonstrating an expressed fear of ALCL likely contributed to their subsequent clinical management (OR cosmetic, 1.66; OR reconstructive, 2.17). CONCLUSIONS: Although the risk of ALCL appears to be more concerning to the reconstructive population, both cohorts were equally motivated to have their implants removed. Informing patients about their ALCL risk is crucial to ensure a patient-supported risk reduction plan.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".