Analysis of Delays to Surgery for Cervical Spinal Cord Injuries
Bibliographic record
Abstract
STUDY DESIGN: A retrospective study of surgically treated patients with cervical spinal cord injury (SCI) from the National Trauma Data Bank Research Data Set. OBJECTIVE: To determine how time to surgery differs between SCI subtypes, where delays before surgery occur, and what factors are associated with delays. SUMMARY OF BACKGROUND DATA: Studies have shown that patients with cervical SCI undergoing surgery within 24 hours after injury have superior neurological outcomes to patients undergoing later surgery, with most evidence coming from the incomplete SCI subpopulation. METHODS: Surgically treated patients with cervical SCI from 2011 and 2012 were identified in National Trauma Data Bank Research Data Set and divided into subpopulations of complete, central, and other incomplete SCIs. Relationships between surgical timing and patient and injury characteristics were analyzed using multivariate regression. RESULTS: A total of 2636 patients with cervical SCI were identified: 803 with complete SCI, 950 with incomplete SCI, and 883 with central SCI. The average time to surgery was 51.1 hours for patients with complete SCI, 55.3 hours for patients with incomplete SCI, and 83.1 hours for patients with central SCI. Only 44% of patients with SCI underwent surgery within the first 24 hours after injury, including only 49% of patients with incomplete SCI.The vast majority of time between injury and surgery was after admission, rather than in the emergency department or in the field. Upper cervical SCIs and greater Charlson Comorbidity Index were associated with later surgery in all 3 SCI subpopulations. CONCLUSION: The majority of patients with SCI do not undergo surgery within the first 24 hours after injury, and the majority of delays occur after inpatient admission. Factors associated with these delays highlight areas of focus for expediting care in these patient populations. LEVEL OF EVIDENCE: 4.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".