Time to Surgery Following Complete Cervical Spinal Cord Injury: Evolution of Clinical Practice Patterns Over a Decade from 2010 to 2020 Across North American Trauma Centers
Bibliographic record
Abstract
This study aims to quantify the change in time to surgery for treatment of complete traumatic cervical spinal cord injury (SCI) patients in American College of Surgeons accredited trauma centers across North America over the last decade (2010-2020). This multi-center retrospective observational cohort study used data from the Trauma Quality Improvement Program from 2010 to 2020. All surgically treated patients with complete traumatic cervical SCI were included. Primary outcome was time to spine surgery from treating hospital arrival in hours. Both descriptive statistics and a multi-variable Poisson regression model clustering standard of errors by each included trauma center were used to evaluate and quantify the annual change in time to surgical intervention. The study included 6855 complete traumatic cervical SCI patients managed across 484 trauma centers in North America. Median time to spine surgery was 14.6 h. A total of 4618 patients (67.3%) underwent surgical intervention within 24 h from hospital arrival. From 2010 to 2020, median time to surgery decreased by an average 0.6 h (±0.15) per year. A multi-variable adjusted model for time to surgery demonstrated a significant downward annual reduction of 5% in time to surgery between the years 2010 and 2020 (Incidence rate ratio = 0.95; 95% Confidence Interval: 0.93-0.96). This study provides compelling real-world based quantification of the change in time to surgical intervention following traumatic cervical SCI. A significant decreasing annual trend pertaining to surgical timing across trauma centers in North America over the past decade was demonstrated.
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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 | 0.001 | 0.004 |
| 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 | 0.000 | 0.001 |
| 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".