Importance and Presence of High-Quality Evidence for Clinical Decisions in Neurosurgery: International Survey of Neurosurgeons
Bibliographic record
Abstract
BACKGROUND: The publication rate of neurosurgical guidelines has increased tremendously over the past decade; however, only a small proportion of clinical decisions appear to be based on high-quality evidence. OBJECTIVE: The aim was to evaluate the evidence available within neurosurgery and its value within clinical practice according to neurosurgeons. METHODS: A Web-based survey was sent to 2552 neurosurgeons, who were members of the European Association of Neurosurgical Societies. RESULTS: The response rate to the survey was 6.78% (173/2552). According to 48.6% (84/173) of the respondents, neurosurgery clinical practices are based on less evidence than other medical specialties and not enough high-quality evidence is available; however, 84.4% (146/173) of the respondents believed neurosurgery is amenable to evidence. Of the respondents, 59.0% (102/173) considered the neurosurgical guidelines in their hospital to be based on high-quality evidence, most of whom considered their own treatments to be based on high-quality (level I and/or level II) data (84.3%, 86/102; significantly more than for the neurosurgeons who did not consider the hospital guidelines to be based on high-quality evidence: 55%, 12/22; P<.001). Also, more neurosurgeons with formal training believed they could understand, criticize, and interpret statistical outcomes presented in journals than those without formal training (93%, 56/60 and 68%, 57/84 respectively; P<.001). CONCLUSIONS: According to the respondents, neurosurgery is based on high-quality evidence less often than other medical specialties. The results of the survey indicate that formal training in evidence-based medicine would enable neurosurgeons to better understand, criticize, and interpret statistical outcomes presented in journals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.063 | 0.772 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".