Canadian breast implant cohort: Extended follow‐up of cancer incidence
Bibliographic record
Abstract
Cosmetic breast implants are not associated with increased breast cancer incidence, but variations of risk according to implant characteristics are still poorly understood. As well, the assessment of cancer risk for sites other than breast needs to be clarified. The purpose of this study was to fill these research gaps. This study presents an extended analysis of 10 more years of follow-up of a large Canadian cohort of women who received either cosmetic breast implants (n = 24,558) or other cosmetic surgery (15,893). Over 70% of the implant cohort was followed for over 20 years. Cancer incidence among implant women was compared to those of controls using multivariate Poisson models and the general female population using the standardized incidence ratios (SIRs). Women with breast implants had reduced rates of breast and endometrial cancers compared to other surgery women. Subglandular implants were associated to a reduced rate of breast cancer compared to submuscular implants [incidence rate ratio (IRR) = 0.78, 95% confidence interval (CI) = 0.63-0.96] and this reduction persisted over time. We observed a sevenfold increased rate (IRR = 7.36, 95% CI = 1.86-29.12) of breast cancer in the first 5 years after the date of surgery for polyurethane-coated subglandular implant women but this IRR decreased progressively over time (p value for trend = 0.02). We also observed no increased risk of rarer forms of cancer among augmented women. A reduction in breast cancer incidence was observed for women with subglandular implants relative to women with submuscular implants. Possible increase of breast cancer incidence shortly after breast augmentation with polyurethane implants needs to be verified.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".