Wrong-sided and wrong-level neurosurgery: a national survey
Bibliographic record
Abstract
OBJECT: Perhaps the single greatest error that a surgeon hopes to avoid is operating at the wrong site. In this report, the authors describe the incidence and possible determinants of incorrect-site surgery (ICSS) among neurosurgeons. METHODS: The authors asked neurosurgeons to complete an anonymous survey. These surgeons were asked to report the number of craniotomies and lumbar and cervical discectomies performed during the previous year, as well as whether ICSS had occurred. They were also asked detailed questions regarding the potential determinants of ICSS. RESULTS: There was a 75% response rate and a 68% survey completion rate. Participating neurosurgeons performed 4695 lumbar and 2649 cervical discectomies, as well as 10,203 craniotomies. Based on this self-reporting, the incidence of wrong-level lumbar surgery was estimated to be 12.8 [corrected] occurrences per 10,000 operations. The ICSSs per 10,000 cervical discectomies and craniotomies were 7.6 [corrected] and 2.0, [corrected] respectively. Neurosurgeons recognized fatigue, unusual time pressure, and emergent operations as factors contributing to ICSS. For spine surgery, in particular, unusual patient anatomy and a failure to verify the operative site by radiography were also commonly reported contributors. CONCLUSIONS: Neurosurgical ICSSs do occur, but are rare events. Although there are significant limitations to the survey-based methodology, the data suggest that the prevention of such errors will require neurosurgeons to recognize risk factors and increase the use of intraoperative imaging.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".