Bibliographic record
Abstract
The House of Commons Standing Committee on Health proposed in 2022 to start a national registry for breast implants. Why, and what requirements are needed, will be outlined. Breast implant products are not always in compliance with international norms and standards, and several scandals have occurred because of industry fraud. To trace which patients have defective breast implants, a good registry is an absolute must. Furthermore, some diseases, such as lymphomas, autoimmune diseases, and so-called breast implant illness, are believed to be associated with breast implants. An accurate estimation of how often these diseases occur in patients with breast implants is lacking. A registry in which not only surgical data but also patient-reported outcome measurements are recorded will result in a better understanding of patient outcomes and device performance. The registry should not be a voluntary ("opt-in") registry but a mandatory ("opt-out") registry, in which only the patient (and not the surgeon) has the choice whether to participate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.042 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".