Usefulness of Canadian Public Health Insurance Administrative Databases to Assess Breast and Ovarian Cancer Screening Imaging Technologies for <i>BRCA1/2</i> Mutation Carriers
Bibliographic record
Abstract
PURPOSE: In Canada, recommendations for clinical management of hereditary breast and ovarian cancer among individuals carrying a deleterious BRCA1 or BRCA2 mutation have been available since 2007. Eight years later, very little is known about the uptake of screening and risk-reduction measures in this population. Because Canada's public health care system falls under provincial jurisdictions, using provincial health care administrative databases appears a valuable option to assess management of BRCA1/2 mutation carriers. The objective was to explore the usefulness of public health insurance administrative databases in British Columbia, Ontario, and Quebec to assess management after BRCA1/2 genetic testing. METHODS: Official public health insurance documents were considered potentially useful if they had specific procedure codes, and pertained to procedures performed in the public and private health care systems. RESULTS: All 3 administrative databases have specific procedures codes for mammography and breast ultrasounds. Only Quebec and Ontario have a specific procedure code for breast magnetic resonance imaging. It is impossible to assess, on an individual basis, the frequency of others screening exams, with the exception of CA-125 testing in British Columbia. Screenings done in private practice are excluded from the administrative databases unless covered by special agreements for reimbursement, such as all breast imaging exams in Ontario and mammograms in British Columbia and Quebec. There are no specific procedure codes for risk-reduction surgeries for breast and ovarian cancer. CONCLUSION: Population-based assessment of breast and ovarian cancer risk management strategies other than mammographic screening, using only administrative data, is currently challenging in the 3 Canadian provinces studied.
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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 | 0.008 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.016 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".