Recommendations for a Canadian Breast Implant Registry
Bibliographic record
Abstract
Introduction: Use of breast implants has nearly doubled for reconstructive and aesthetic surgery throughout North America. This growing demand highlights the need for breast implant registries to monitor safety and quality of care. Despite wide adoption of implant registries in other countries, there is currently no Canadian system to track implantation of breast prostheses. This review aimed to inform the development and implementation of a Canadian breast implant registry (BIR). Methods: A systematic review was conducted to include searches of Medline Ovid, Web of Science, Embase Ovid and grey literature databases. Data were extracted for: patient participation, registry structure, data quality, funding and reporting outputs. Results: Of 1577 articles, a total of 19 met inclusion criteria. The Dutch, Australian, American, German, United Kingdom and Korean implant registries were analyzed. Opt-out systems were commonly used and correlated with higher capture rates. Data input relied on physician or surgeon data entry. Funding was private for the Dutch BIR through a patient or insurance surcharge, and government funding was used in the United Kingdom, Australian and Korean registries. Finally, all registries disseminated outcomes via annual reports. Conclusion: Based on strategies used in existing registries, it is recommended a Canadian BIR have an opt-out structure, funding from a combination of government or private stakeholders, use a standardized data set and annual reporting.
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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 | 0.061 | 0.149 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.024 | 0.006 |
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".