Medico-Legal Cases in Breast Imaging in Canada: A Trend Analysis
Bibliographic record
Abstract
Purpose: Breast imaging accounts for a large proportion of medico-legal cases involving radiologists in several countries and may be a disincentive to breast imaging. As this has not been well studied in Canada, we evaluated the key medico-legal issues of breast imaging in Canada and their implications for health care providers and patient safety. Methods: In collaboration with Canadian Medical Protective Association (CMPA), we obtained information from the medico-legal repository, including civil-legal, medical regulatory authority (College) and hospital complaints occurring between 2002-2021. Canadian Classification of Health Interventions (CCI) codes were used for breast imaging and biopsy. Trend analysis was done comparing cases involving breast imaging/biopsy to all cases where a radiologist was named. Results: Radiologists were named in 3108 medico-legal cases, 188 (6%, 188/3108) of which were CCI coded for breast imaging or biopsy. Factors related to radiologists were most frequent (64%, 120/188), followed by team (23.4%, 44/188) and system (6.9%, 13/188). Equal representation of male and female radiologists was found (IRR = 1.22; 95% CI: .89, 1.56). In a 10-year test window from 2006 - 2015 we identified an increasing trend for all cases involving radiologists ( P = 0,0128) but a decreasing trend for cases coded with breast imaging or biopsy ( P = 0,0099). Conclusions: A significant decrease in cases involving breast imaging were found from 2006-2015, accounting for 6% of the medico-legal cases. The lower risk of breast imaging medico-legal issues may encourage more radiologists in breast imaging.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".