Medico-legal risk of infectious disease physicians in Canada: A retrospective review
Bibliographic record
Abstract
Objective: There is little known about the medico-legal risk for infectious disease specialists in Canada. The objective of this study was to identify the causes of these medico-legal risks with the goal of improving patient safety and outcomes. Methods: A 10-year retrospective analysis of Canadian Medical Protective Association (CMPA) closed medico-legal cases from 2012 to 2021 was performed. Peer expert criticism was used to identify factors that contributed to the medico-legal cases at the provider, team, or system level, and were contrasted with the patient complaint. Results: During the study period there were 571 infectious disease physician members of the CMPA. There were 96 patient medico-legal cases: 45 College complaints, 40 civil legal matters, and 11 hospital complaints. Ten cases were associated with severe patient harm or death. Patients were most likely to complain about perceived deficient assessments (54%), diagnostic errors (53%), inadequate monitoring or follow-up (20%), and unprofessional manner (20%). In contrast, peer experts were most critical of the areas of diagnostic assessment (20%), deficient assessment (10%), failure to perform test/intervention (8%), and failure to refer (6%). Conclusion: While infectious disease physicians tend to have lower medico-legal risks compared to other health care providers, these risks still do exist. This descriptive study provides insights into the types of cases, presenting conditions, and patient allegations associated with their practice.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".