Perception of Implants among Breast Reconstruction Patients in Montreal
Bibliographic record
Abstract
Background: In light of the recent surge of media coverage and social media influence regarding breast implants, it is essential to understand patients’ concerns and misconceptions so that we can better serve them. Methods: The authors designed a survey study for assessing the awareness and perception of patients toward breast implant–associated anaplastic large cell lymphoma (BIA-ALCL) and breast implant illness (BII). In total, 130 patients presenting to the senior author’s breast reconstruction clinic completed the survey. The survey assessed patients’ knowledge on and their perception of BIA-ALCL and BII. Results: “News article” and “Television” were most often selected as sources of information for BIA-ALCL (21% and 20%, respectively) and BII (20% and 25%, respectively). A total of 100 patients (77%) had previous knowledge of BIA-ALCL. Forty-seven percent (n = 47/100) responded that they were unsure of the fate of a person diagnosed with BIA-ALCL, and 25% (n = 25/100) were unaware of the association between BIA-ALCL and specific implant type. Patients who were unaware of BIA-ALCL prognosis reported being less likely to receive breast implants in the future ( P = 0.012, χ 2 = 19.48). Eighty-nine patients (68%) had previous knowledge of BII. A total of 60 symptoms were mentioned by patients, with “Fatigue” (12%, n = 26) being cited the most often. Conclusions: The present survey highlights the importance for plastic surgeons to frequently discuss these entities with their patients. This should be done despite the obscurity of BII, in an effort to offer the best available evidence to our patients.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".