Analysis of Allergan’s Biocell Implant Recall in a Major University Breast Center
Bibliographic record
Abstract
BACKGROUND: In May 2019, Health Canada released a national recall of all macrotextured breast implants that later became international in July 2019 regarding increasing accounts of suspected breast implant-associated anaplastic large cell lymphoma. In Canada, this recall targeted Allergan's Biocell implants. This report presents the postmortem of this comprehensive single-center recall, which had to be undertaken in a limited time. METHODS: Four months after the beginning of the recall, the authors analyzed the transcript of meetings to characterize the team assembled during the recall. Then, to reconstruct the systemic work plan as well as the crucial steps and actors of the recall process, a chronologic table of the 5 meetings held during the recall, agendas and transcripts of every meeting, electronic correspondences, and other documents created during the recall were consulted. RESULTS: Between 1996 and 2018, 1260 women were affected by the recall, meaning that they received Allergan's macrotextured implants. Ninety-two patients underwent explantation of the device or will undergo implant explantation. To this day, no patient was diagnosed with breast implant-associated anaplastic large cell lymphoma. CONCLUSIONS: Our center's experience highlights the utmost importance of building a national breast implants registry. We recommend breast centers to develop preestablished crisis centers and train staff to better prepare for future device recalls and minimize waste of time. Finally, we believe that implants should be identified based on the characteristics rather than their brand name.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".