A National Survey to Assess the Population’s Perception of Breast Implant-Associated Anaplastic Large Cell Lymphoma and Breast Implant Illness
Bibliographic record
Abstract
BACKGROUND: The goal of this study was to gauge the public's general perception of breast implants, levels of concern, spontaneous word associations, and misperceptions that might need to be addressed by plastic surgeons regarding breast implant-associated anaplastic large cell lymphoma (BIA-ALCL) and breast implant illness (BII). METHODS: An anonymous survey was completed by a total of 979 female participants in the United States by means of Amazon Mechanical Turk. RESULTS: Over 91 percent of participants indicated that they had never heard the term BIA-ALCL. Of the respondents who were aware of the term, 37.21 percent reported being moderately or extremely concerned about BIA-ALCL and 85.4 percent were less likely to recommend breast implants to a friend. Awareness of BII was significantly higher at 50.9 percent, whereas almost 40 percent of participants reported being either moderately or extremely concerned about BII. Over 78 percent of participants were less likely to recommend breast implants to a friend because of BII. The most common word association with BII was "pain," followed by "cancer." The terms "cancer" and "scary" were the two most common word associations with BIA-ALCL. A significant overlap in word associations was observed between BIA-ALCL and BII, potentially representing a lack of distinction between the two terms. The survey demonstrated a paucity of important knowledge within the general population; notably, 71 percent of respondents who were not aware that, to date, only textured implants/expanders were associated with BIA-ALCL. CONCLUSION: These findings support the need for further targeted awareness to remedy existing misperceptions and fill the knowledge gaps relating to BII and BIA-ALCL.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".