Defensive medicine among neurosurgeons in the Netherlands: a national survey
Bibliographic record
Abstract
OBJECTIVE: In defensive medicine, practice is motivated by legal rather than medical reasons. Previous studies have analyzed the correlation between perceived medico-legal risk and defensive behavior among neurosurgeons in the United States, Canada, and South Africa, but not yet in Europe. The aim of this study is to explore perceived liability burdens and self-reported defensive behaviors among neurosurgeons in the Netherlands and compare their practices with their non-European counterparts. METHODS: A survey was sent to 136 neurosurgeons. The survey included questions from several domains: surgeon characteristics, patient demographics, type of practice, surgeon liability profile, policy coverage, defensive practices, and perception of the liability environment. Survey responses were analyzed and summarized. RESULTS: Forty-five neurosurgeons filled out the questionnaire (response rate of 33.1%). Almost half (n = 20) reported paying less than 5% of their income to annual malpractice premiums. Nearly all respondents view their insurance premiums as a minor or no burden (n = 42) and are confident that in their coverage is sufficient (n = 41). Most neurosurgeons (n = 38) do not see patients as "potential lawsuits". CONCLUSIONS: Relative to their American peers, Dutch neurosurgeons view their insurance premiums as less burdensome, their patients as a smaller legal threat, and their practice as less risky in general. They are sued less often and engage in fewer defensive behaviors than their non-European counterparts. The medico-legal climate in the Netherlands may contribute to this difference.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".