Incidence of Unintended Durotomy in Spine Surgery Based on 108 478 Cases
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
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.
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 | 0.000 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it