A Multicenter Study of the Presentation, Treatment, and Outcomes of Cervical Dural Tears
Bibliographic record
Abstract
STUDY DESIGN: Retrospective multicenter case series study. OBJECTIVE: Because cervical dural tears are rare, most surgeons have limited experience with this complication. A multicenter study was performed to better understand the presentation, treatment, and outcomes following cervical dural tears. METHODS: Multiple surgeons from 23 institutions retrospectively identified 21 rare complications that occurred between 2005 and 2011, including unintentional cervical dural tears. Demographic data and surgical history were obtained. Clinical outcomes following surgery were assessed, and any reoperations were recorded. Neck Disability Index (NDI), modified Japanese Orthopaedic Association (mJOA), Nurick classification (NuC), and Short-Form 36 (SF36) scores were recorded at baseline and final follow-up at certain centers. All data were collected, collated, and analyzed by a private research organization. RESULTS: < .05) in mJOA and NuC scores, but not NDI or SF36 scores. No specific baseline or operative factors were found to be associated with the occurrence of dural tears. In most cases, no further postoperative treatments of the dural tear were required, while there were 13 patients (12%) that required subsequent treatment of cerebrospinal fluid drainage. Analysis of those requiring further treatments did not identify an optimum treatment strategy for cervical dural tears. CONCLUSIONS: In this multicenter study, we report our findings on the largest reported series (n = 109) of cervical dural tears. In a vast majority of cases, no subsequent interventions were required and no clinical sequelae were observed.
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.
How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".