Does Timing of Surgery Affect Hospitalization Costs and Length of Stay for Acute Care following a Traumatic Spinal Cord Injury?
Bibliographic record
Abstract
Although there is a trend toward performing early surgery for traumatic spinal cord injury (SCI), it remains unclear whether this tendency leads to decreased costs and length of stay (LOS) for acute care. This study determined the impact of surgical timing on costs and LOS after a traumatic SCI. A total of 477 consecutive patients sustaining an acute traumatic SCI and receiving surgery at a level I trauma center were included. A general linear model was used to assess the relationship among costs, LOS, and surgical delay, while accounting for various sociodemographic and clinical covariables. The analysis was also repeated with surgical delay dichotomized within 24 h or later after the trauma. Mean costs and LOS for all patients were respectively 24,156 ± 17,244 $CAD and 35.0 ± 39.4 days. The costs of acute care hospitalization were related to the surgical delay between the trauma and the surgery, in addition to age, injury severity score (ISS), American Spinal Injury Association (ASIA) grade, and neurological level. LOS was associated with the surgical delay dichotomized into two groups (<24 vs. ≥24 h), as well as with age, ISS, ASIA grade, and neurological level. This study suggests that resource utilization in terms of costs and LOS for the acute hospitalization is decreased with early surgery after an acute traumatic SCI, particularly if the procedure is performed within 24 h following the trauma. Performing the surgery as early as possible when the patient is cleared for surgery could lower the financial burden on the healthcare system, while optimizing the neurological recovery.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".