THE AUTHORS REPLY
Bibliographic record
Abstract
We thank Didie and Phillips (1) for their interest in our work (2) and their comment on the relevance of body dysmorphic disorder (BDD) among women who receive breast implants for cosmetic reasons. We agree that mental disorders, such as BDD, may have contributed to our observation of increased suicide rates among both the breast implant and other plastic surgery patients in our cohort. Although the prevalence of BDD in the general population is estimated to be between 1 percent and 2 percent (3–5), the review by Crerand et al. (6) suggests that the prevalence of BDD among patients who present for cosmetic procedures is between 7 percent and 15 percent. Moreover, their review of the literature suggests that patients with BDD do not benefit from receiving cosmetic procedures. We share the view of Didie and Phillips (1) that future cohort studies of breast implant patients should endeavor to collect information on their mental health at baseline. However, there are important methodological considerations to evaluating BDD as a risk factor for suicide in such patient populations. In particular, sample size is a critical consideration. In our study of 24,558 women with breast implants, there were only a total 58 suicides. Therefore, even had baseline data been collected on mental disorders in our patient population, with an anticipated BDD prevalence of 7–15 percent, we would have relatively poor statistical power to characterize suicide risk.
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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 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.029 | 0.039 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
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".