Breast Implant-associated Anaplastic Large Cell Lymphoma: A Canadian Surgical Oncology Survey
Bibliographic record
Abstract
BACKGROUND: Breast implant-associated anaplastic large cell lymphoma (BIA-ALCL) awareness has increased, resulting in concerns regarding the safety of implant-based reconstruction. Breast cancer patients are first seen by surgical oncologists, who are therefore potentially the first health-care professionals to encounter concerns regarding BIA-ALCL. We therefore surveyed surgical oncologists on their understanding of BIA-ALCL to better assess potential effects on plastic surgery practice. METHODS: An anonymous web-based survey consisting of 9 multiple-choice questions was sent to breast surgical oncologists that are members of the Canadian Society of Surgical Oncology (n = 135). RESULTS: Forty-two members responded (n = 42/135, 31%) and all participants were aware of BIA-ALCL. All participants reported that BIA-ALCL has not deterred them from referring patients for implant-based reconstruction. Twenty-two respondents (52%) discuss BIA-ALCL with their patients and 21% (n = 9) believe that BIA-ALCL typically follows a metastatic course. Eight respondents (19%) reported having a poor understanding of BIA-ALCL, while 14% (n = 6) were unable to identify the link to textured implants. There were no statistical differences based on case-load volume. CONCLUSIONS: Approximately half of the respondent Canadian breast surgical oncologists discuss BIA-ALCL with their patients, yet there is a knowledge gap in terms of the epidemiology and clinical-pathological course of BIA-ALCL. It is of utmost importance to ensure that the plastic surgery community aims at including surgical oncologist colleagues in educational platforms regarding BIA-ALCL to ensure collaboration and unity in an effort to offer the most accurate information to patients, and prevent misinformation that may deter patients from seeking implant-based reconstruction.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".