Preventing Wrong-Level Spine Surgery
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.027 | 0.007 |
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 it