Incidence of Unintended Durotomy in Spine Surgery Based on 108 478 Cases
Bibliographic record
Abstract
BACKGROUND: Unintended durotomy is a common complication of spinal surgery. However, the incidences reported in the literature vary widely and are based primarily on relatively small case numbers from a single surgeon or institution. OBJECTIVE: To provide spine surgeons with a reliable incidence of unintended durotomy in spinal surgery and to assess various factors that may influence the risk of durotomy. METHODS: We assessed 108,478 surgical cases prospectively submitted by members of the Scoliosis Research Society to a deidentified database from 2004 to 2007. RESULTS: Unintended durotomy occurred in 1.6% (1745 of 108 478) of all cases. The incidence of unintended durotomy ranged from 1.1% to 1.9% on the basis of preoperative diagnosis, with the highest incidence among patients treated for kyphosis (1.9%) or spondylolisthesis (1.9%) and the lowest incidence among patients treated for scoliosis (1.1%). The most common indication for spine surgery was degenerative spinal disorder, and among these patients, there was a lower incidence of durotomy for cervical (1.0%) vs thoracic (2.2%; P = .01) or lumbar (2.1%, P < .001) cases. Scoliosis procedures were further characterized by etiology, with the highest incidence of durotomy in the degenerative subgroup (2.2% vs 1.1%; P < .001). Durotomy was more common in revision compared with primary surgery (2.2% vs 1.5%; P < .001) and was significantly more common among elderly (> 80 years of age) patients (2.2% vs 1.6%; P = .006). There was a significant association between unintended durotomy and development of a new neurological deficit (P < .001). CONCLUSION: Unintended durotomy occurred in at least 1.6% of spinal surgeries, even among experienced surgeons. Our data provide general benchmarks of durotomy rates and serve as a basis for ongoing efforts to improve safety of care.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".