Evolution of Real-World Clinical Practice in Time to Surgery Following Thoracolumbar Spinal Cord Injury: An Observational Study of North American Trauma Centers from 2010 to 2020
Bibliographic record
Abstract
This study aims to estimate real-world clinical practice trends in time to surgery following thoracolumbar spinal cord injury (SCI) in trauma centers across North America over the last decade (2010-2020). A multi-center retrospective observational study was conducted using Trauma Quality Improvement Program data from 2010 to 2020. All surgically treated patients with thoracic and lumbar SCI were included. Descriptive plots and a multivariable Poisson regression model with time to spine surgery as the primary outcome were constructed. This study included 4350 adult patients with complete SCI surgically treated across 449 trauma centers. Within this group, 3978 (91.4%) patients were diagnosed with thoracic SCI and 372 (8.6%) patients were diagnosed with lumbar SCI. The overall mean time to surgery was 31.6 h (±34.1). Early surgery (≤24 h) was performed in 2599 patients (59.7%). An estimated annual reduction of 1.6 h in time to surgery was demonstrated over the study period, starting initially at a mean of 47.6 h (±40.6) in 2010, and reaching a mean of 25.3 h (±30) in 2020. Multivariable Poisson regression adjusting for patient, injury, and institution confounders, demonstrated a significant decrease in time to surgery by 5% per year over the study period (incidence rate ratios [IRR] = 0.95, 95% confidence interval [CI]: 0.93-0.96). Moreover, in a secondary analysis including 3270 patients with incomplete thoracolumbar SCI, a comparable significant annual reduction in time to surgery was demonstrated (IRR = 0.93, 95% CI: 0.91-0.94). This study provides real-world data on practice pattern trends with respect to time to spine surgery following traumatic thoracolumbar SCI. Over the years from 2010 to 2020, we found a significant reduction in time to surgery across trauma centers in North America.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".