P.206 An examination of risk factors and outcomes of iatrogenic dural tears in southern New Brunswick
Bibliographic record
Abstract
Background: Dural tears (DT) are relatively common spine surgery complications, increasing risks of cerebrospinal fluid leaks, adverse events, and prolonged hospitalization. This study sought to identify DT predictors and compare postoperative outcomes including adverse events, revision, emergency room (ER) care, and length of stay between DT and non-DT cohorts. Methods: Retrospective analysis of elective spine surgery patients at a single tertiary centre. Variables included demographics, DT repair techniques, risk factors, post-operative adverse events, ER care within 30 days post-op, and revision. Binary logistic regression was used to analyze risk factors while hierarchical logistic and linear regressions analyzed postoperative events. Results: 6.6% of patients experienced DTs, with patches used in 40% of repairs. Age was a risk factor for DT (EXP(B)=1.039, CI [1.016, 1.063]), while minimally invasive surgery (MIS) (EXP(B)=0.521, CI [.297, .912]) reduced risk. DTs were associated with increased rates of cardiac arrest (EXP(B) = 3.966, CI [1.046, 15.033]), urinary retention (EXP(B)=2.408, CI [1.218, 4.759]), revision (EXP(B)=4.574, CI [1.941, 10.779]), ER visits (EXP(B)=1.975, CI [1.020, 3.826]), and length of stay (B=3.42, p<0.001). Conclusions: MIS seems to be associated with decreased DT risk. DTs are also associated with post-operative cardiac arrest, urinary retention, required revision surgery, and visits to the ER within 30 days post-op.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".