Earlier Surgery Reduces Complications in Acute Traumatic Thoracolumbar Spinal Cord Injury: Analysis of a Multi-Center Cohort of 4108 Patients
Bibliographic record
Abstract
Early surgical intervention to decompress the spinal cord and stabilize the spinal column in patients with acute traumatic thoracolumbar spinal cord injury (TLSCI) may lessen the risk of developing complications and improve outcomes. However, there has yet to be agreement on what constitutes "early" surgery; reported thresholds range from 8 to 72 h. To address this knowledge gap, we conducted an observational cohort study using data from the American College of Surgeons (ACS) Trauma Quality Improvement Program (TQIP) from 2010 to 2016. The association between time from hospital arrival to surgical intervention and risk of major complications was assessed using restricted cubic splines. Propensity score matching was then used to assess the association between delayed surgery and risk of complications. Across 354 trauma centers 4108 adult TLSCI patients who underwent surgery were included. Median time-to-surgery was 18.8 h (interquartile range [IQR]: 7.4-40.9 h). The spline model suggests the risk of major complication rises consistently after a 12-h surgical wait-time. After propensity score matching, the odds of major complication were significantly lower for those receiving surgery within 12 h (odds ratio [OR] 0.77, 95% confidence interval [CI]: 0.64 to 0.94). This was also true for immobility-related complications (OR 0.79, 95% CI: 0.64 to 0.97). Patients in the early group spent 1.5 fewer days in the critical care unit on average (95% CI: -2.09 to -0.88). Although surgery within 12 h may not always be feasible, these data suggest that whenever possible surgeons should strive to reduce the amount of time between hospital arrival and surgical intervention, and health care systems should support this endeavor.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".