Medico-legal cases involving gastroenterologists in Canada between 2017 and 2021
Bibliographic record
Abstract
Background: Gastroenterology may be a medical specialty with higher-than-average medico-legal risk. We evaluated the characteristics of medico-legal proceedings relating to the delivery of gastroenterology medical care in Canada during a five-year time period. Methods: We used a repository of Canadian medico-legal cases to identify cases between 2017 and 2021 involving a gastroenterologist. We analyzed patient, provider, team, and system contributing factors using a previously published Contributing Factors Framework and patient harm using a previously published coding system. Results: We identified 223 cases involving 229 gastroenterologists with no preponderance by years of experience. Gastroenterologists had a higher rate of civil legal actions than the average for all other physician specialties in the database. 59% involved patients older than 50 years of age, 10% with digestive tract malignancies, and 10% with IBD. 51% of involved patients had a healthcare-related harm that had a negative effect on their health or quality of life. 35% had avoidable harm. Patients most commonly reported a perception of deficient assessment (35%), communication breakdowns (27%), unprofessional manner (25%), diagnostic error (22%), and inadequate monitoring or follow-up (20%). 50% of cases were criticized by peer experts, of which they deemed 45% involved communication breakdown with patients, 38% involved clinical decision-making, 30% situational awareness, 25% documentation, and 15% communication among providers. Conclusions: Communication issues remain a major contributing factor to medico-legal cases involving gastroenterologists. Integrated risk-reduction strategies may include enhancing diagnostic rigor through improved clinical protocols and decision support tools and strengthening communication at all levels of care.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".