Do Breast Implants Adversely Affect Prognosis among Those Subsequently Diagnosed with Breast Cancer? Findings from an Extended Follow-Up of a Canadian Cohort
Bibliographic record
Abstract
BACKGROUND: Cosmetic breast implants may impair the ability to detect breast cancers. The aims of this study were to examine whether implants and implant characteristics are associated with more advanced breast tumors at diagnosis and poorer survival. METHODS: Study population includes all invasive breast cancer cases diagnosed during follow-up of the large Canadian Breast Implant Cohort. A total of 409 women with cosmetic breast implants and 444 women with other cosmetic surgery were diagnosed with breast cancer. These women were compared for stage at diagnosis using multinomial logistic regression models. Cox proportional hazards regression models were used for breast cancer-specific mortality analyses. Comparisons were also conducted according to implant characteristics. RESULTS: Compared with women with other cosmetic surgery, those with cosmetic breast implants had at later stage breast cancer diagnosis (OR of having stage III/IV vs. stage I at diagnosis: 3.04, 95% confidence interval (CI): 1.81-5.10; P < 0.001). A nonstatistically significant increase in breast cancer-specific mortality rate for women with breast implants relative to surgical controls was observed (HR = 1.32, 95% CI: 0.94-1.83, P = 0.11). No statistically significant differences in stage and breast cancer mortality were observed according to implant characteristics. CONCLUSIONS: At diagnosis, breast cancers tended to be at more advanced stages among women with cosmetic breast implants. Breast cancer-specific survival was lower in these women although the reduction did not reach statistical significance. IMPACT: Further investigations of the effect of breast implants on breast cancer prognosis are warranted.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".