Interventions to Optimize Spinal Cord Perfusion in Patients with Acute Traumatic Spinal Cord Injuries: A Systematic Review
Bibliographic record
Abstract
Interventions to optimize spinal cord perfusion via support of mean arterial pressure (MAP) or spinal cord perfusion pressure (SCPP) are thought to play a critical role in the management of patients with acute traumatic spinal cord injuries, but there is ongoing controversy about efficacy and safety. We aimed to determine the effects of optimizing spinal cord perfusion on neurological recovery and risks for adverse events. We searched multiple databases for published and unpublished reports. Two reviewers independently screened articles, extracted data, and evaluated risk of bias. We synthesized data and evaluated confidence in anticipated treatment effects according to the Grades of Recommendation, Assessment, Development, and Evaluation (GRADE) approach. We identified 20 eligible observational studies and 1 eligible randomized controlled trial. According to low or very low quality evidence, the effect of MAP support on neurological recovery after acute traumatic spinal cord injury is uncertain, and the use of vasopressors to support MAP may be associated with increased rates of predominantly cardiac adverse events. Increased SCPP appears likely to be associated with improved neurological recovery, but SCPP monitoring via intradural catheters at the anatomical site of injury may involve increased risks of cerebrospinal fluid leakage requiring revision surgery or pseudomeningocele. No study directly compared the effects of specific MAP goal ranges, SCPP ranges, SCPP monitoring techniques, or durations of treatment. Very low quality evidence suggests that norepinephrine may have less risk of adverse events than dopamine. The current literature is insufficient to make strong recommendations about interventions to support spinal cord perfusion via MAP or SCPP goals in patients with acute traumatic spinal cord injuries. Data are compatible with a variety of treatment decisions, and individualized approaches may be optimal. Further investigation to clarify the risks, benefits, and alternatives to MAP or SCPP support in this population is warranted.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
| grok | Meta-epidemiology (broad) Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
| opus | Meta-epidemiology (broad) Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | medium |
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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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, unvalidatedLabeled directly by 3 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".