Defensive Medicine in Neurosurgery: The Sub-Saharan Africa Experience
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: This study investigates the prevalence and determinants of defensive medicine among neurosurgeons in Sub-Saharan Africa (SSA). It examines how economic, cultural, and legal factors unique to SSA influence these practices, providing insights to guide regional policy-making and medical education. METHODS: A cross-sectional survey of 71 neurosurgeons in SSA was conducted via WhatsApp, LinkedIn, and conferences. The questionnaire, adapted to the SSA context from a Canadian study, explored demographics, practice types, liability profiles, defensive behaviors, and perceptions of the medicolegal environment. Data were analyzed using descriptive statistics in R software. RESULTS: Among 71 respondents, 91.5% were men, and 29.6% were undergoing residency or fellowship training. All respondents reported engaging in at least 1 defensive medical behavior, with varying degrees of frequency. Common strategies included patient discussions (24.2%) and specialist referrals (16.7%). Economic and resource limitations constrained practices such as ordering imaging (17.5%) and prescribing medications (10.8%). Despite perceived medicolegal risks, 93% of participants reported no lawsuits in the past 3 years. CONCLUSION: Defensive medicine among neurosurgeons in SSA is less prevalent and intense than in high-income regions. Unique economic constraints, cultural norms, and weaker legal pressures limit defensive behaviors. These findings highlight the need for context-specific policies and educational strategies to balance medicolegal risk management with resource limitations in SSA.
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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 | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".