P.094 Spinal dural repair: a Canadian questionnaire
Bibliographic record
Abstract
Background: Iatrogenic dural tear a complication of spinal surgery with significant morbidity and cost to the healthcare system. The optimal management is unclear, and therefore we aimed to survey current practices among Canadian practitioners. Methods: A questionnaire was administered to members of the Canadian Neurological Surgeon’s Society designed to explore methods of closure of iatrogenic durotomy. Results: Spinal surgeons were surveyed with a 55% response rate (n=91). For pinhole sized tears there is a trend toward sealant fixation(36.7%). Medium and large sized tears are predominantly closed with sutures and sealant(67% and 80%, respectively). Anterior tears are managed using sealant alone(48%). Posterior tears are treated with a combination of sutures and sealant(73.8%). Nerve root tears are treated with either sealant alone(50%). Most respondents recommended bed rest for at least 24 hours in the setting of medium(73.2%) and large(89.1%) dural tears. Conclusions: This study elucidates the areas of uncertainty with regard to iatrogenic dural tear management. There is disagreement regarding management of anterior and nerve root tears, pin-hole sized tears in any location of the spine, and whether patients should be admitted to hospital or on bed-rest following a pin-hole sized dural tear. There is a need for a robust comparative research study of dural repair strategies.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".