Mortality among Canadian Women with Cosmetic Breast Implants
Bibliographic record
Abstract
There is widespread concern about possible long-term health effects among women who have received breast implants for cosmetic purposes; few studies have reported on the mortality patterns of such women. The authors examined cause-specific mortality in a cohort of 24,558 women with breast implants and 15,893 women who underwent other plastic surgery procedures in Ontario and Quebec, Canada, between 1974 and 1989. Deaths through 1997 were identified through linkage to the national mortality database. The authors compared the mortality of women who received implants with that of the general population by using standardized mortality ratios; Poisson regression was used to perform internal cohort comparisons. Overall mortality was lower among women who received breast implants relative to the general population (standardized mortality ratio = 0.74, 95% confidence interval: 0.68, 0.81). In contrast, higher suicide rates were observed in both the implant (standardized mortality ratio = 1.73, 95% confidence interval: 1.31, 2.24) and other plastic surgery (standardized mortality ratio = 1.55, 95% confidence interval: 1.07, 2.18) patients. No differences in mortality were found between the implant and other surgeries group for any of the 20 causes of death examined. Findings suggest that breast implants do not directly increase mortality in women. Further work is needed to evaluate risk factors for suicide among women who undergo elective cosmetic surgery.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".