Preventing Wrong-Level Spine Surgery
Bibliographic record
Abstract
IMPORTANCE: Wrong-level spine surgery (WLSS), a medical error in which a surgeon operates at an unintended vertebral level, is considered a "never event." However, it continues to be a problem in spine surgery today despite the implementation of preventive measures such as the Universal Protocol. The consequences of this event are severe for both the afflicted patient and the treating physician and may result not only in physical harm but also in costly medicolegal proceedings. OBSERVATIONS: While WLSS incidence varies with the patient population and practice setting, large studies generally report rates below 1%. Given the ubiquity of spine surgery, this remains a concerning number. Risk factors for WLSS can be categorized into three domains: patient factors, imaging issues, and technical issues. Awareness of risk factors allows surgeons to plan for difficulties in level localization. Many techniques for preventing WLSS have been developed, including invasive preoperative marking strategies. Intraoperative radiography or fluoroscopy is necessary but not sufficient for WLSS prevention, in that many errors occur after imaging. The evidence for prevention methods remains of low quality, necessitating future prospective comparison studies. CONCLUSIONS AND RELEVANCE: Consensus has been reached in professional societies: All spine surgeons should implement WLSS prevention protocols. We assess the reported techniques for safer surgery and emphasize one crucial time-out element: the time-out for level localization (TOLL). Addressing WLSS as a problem specific to spine surgery, we show that by using specially tailored prevention strategies, such measures will allow WLSS to become a true never event.
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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 | 0.004 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".