Defensive medicine in neurosurgery: the Canadian experience
Bibliographic record
Abstract
OBJECT Recent studies have examined the impact of perceived medicolegal risk and compared how this perception impacts defensive practices within the US. To date, there have been no published data on the practice of defensive medicine among neurosurgeons in Canada. METHODS An online survey containing 44 questions was sent to 170 Canadian neurosurgeons and used to measure Canadian neurosurgeons' perception of liability risk and their practice of defensive medicine. The survey included questions on the following domains: surgeon demographics, patient characteristics, type of physician practice, surgeon liability profile, policy coverage, defensive behaviors, and perception of the liability environment. Survey responses were analyzed and summarized using counts and percentages. RESULTS A total of 75 neurosurgeons completed the survey, achieving an overall response rate of 44.1%. Over one-third (36.5%) of Canadian neurosurgeons paid less than $5000 for insurance annually. The majority (87%) of Canadian neurosurgeons felt confident with their insurance coverage, and 60% reported that they rarely felt the need to practice defensive medicine. The majority of the respondents reported that the perceived medicolegal risk environment has no bearing on their preferred practice location. Only 1 in 5 respondent Canadian neurosurgeons (21.8%) reported viewing patients as a potential lawsuit. Only 4.9% of respondents would have selected a different career based on current medicolegal risk factors, and only 4.1% view the cost of annual malpractice insurance as a major burden. CONCLUSIONS Canadian neurosurgeons perceive their medicolegal risk environment as more favorable and their patients as less likely to sue than their counterparts in the US do. Overall, Canadian neurosurgeons engage in fewer defensive medical behaviors than previously reported in the US.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".