The Impact of Prehospital Spinal Immobilization in Patients with Penetrating Spinal Injuries: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Penetrating injuries, such as gunshot or stab wounds, may cause spinal cord injuries and require prehospital spinal immobilization (PHSI) to stabilize the spine. However, the use of PHSI in penetrating spinal injuries remains controversial. This systematic review aimed to investigate the efficacy of prehospital PHSI in patients with penetrating trauma. We systematically searched Google Scholar, Medline (PubMed), The Cochrane Central Register of Controlled Trials (CENTRAL), and EMBASE between January 2000 and July 2021. All studies in English that assessed PHSI in patients (>16 years) with penetrating spinal injuries were included. Quality and risk of bias assessments were performed using the modified Newcastle-Ottawa scale. A narrative synthesis and a meta-analysis was conducted. Our search identified 928 studies but only 6 met our inclusion and exclusion criteria. All of the included studies were conducted in the US and the number of patients ranged from 156–75,567 over 3–9 study years. The majority of patients were gunshot or stab wounds. Three studies demonstrated an increased risk of mortality with spinal collars whilst the remaining three studies failed to show any benefits or the benefits remained unproven. All studies were retrospective studies with some risks of bias. This review highlights that the evidence from the literature on PHSI in penetrating trauma outweigh its benefits; thus, its use is discouraged in penetrating spinal trauma. However, further high-quality research is necessary to reach definitive conclusions and to possibly identify suitable alternatives to PHSI for penetrating spinal trauma.
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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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.037 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".