Variability in time to surgery for patients with acute thoracolumbar spinal cord injuries
Bibliographic record
Abstract
There are limited data pertaining to current practices in timing of surgical decompression for acute thoracolumbar spinal cord injury (SCI). We conducted a retrospective cohort study to evaluate variability in timing between- and within-trauma centers in North America; and to identify patient- and hospital-level factors associated with treatment delay. Adults with acute thoracolumbar SCI who underwent decompressive surgery within five days of injury at participating trauma centers in the American College of Surgeons Trauma Quality Improvement Program were included. Mixed-effects regression with a random intercept for trauma center was used to model the outcome of time to surgical decompression and assess risk-adjusted variability in surgery timeliness across centers. 3,948 patients admitted to 214 TQIP centers were eligible. 28 centers were outliers, with a significantly shorter or longer time to surgery than average. Case-mix and hospital characteristics explained < 1% of between-hospital variability in surgical timing. Moreover, only 7% of surgical timing variability within-centers was explained by case-mix characteristics. The adjusted intraclass correlation coefficient of 12% suggested poor correlation of surgical timing for patients with similar characteristics treated at the same center. These findings support the need for further research into the optimal timing of surgical intervention for thoracolumbar SCI.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".