Why Do You Prescribe Methylprednisolone for Acute Spinal Cord Injury? A Canadian Perspective and a Position Statement
Bibliographic record
Abstract
OBJECTIVE: To determine the practice patterns for methylprednisolone administration for patients with acute spinal cord injury (SCI) within the spinal surgery community across Canada, and the reasons behind these patterns. METHODS: Canadian neurological and orthopedic spine surgeons were surveyed at their respective annual meetings with a questionnaire asking seven questions with respect to their practice standards. RESULTS: Sixty surgeons completed the survey representing approximately two-thirds of surgeons treating acute SCI within Canada. The NASCIS III dosing regimen is the most commonly prescribed steroid protocol. However, one-quarter of surgeons do not administer steroids at all. Of those who administer methylprednisolone, most do so because of peer pressure or out of fear of litigation. CONCLUSIONS: The vast majority of spine surgeons in Canada either do not prescribe methylprednisolone for acute SCI, or do so for what might be considered the wrong reasons. These results demonstrate the need for an evidence-based practice guideline. The Canadian Spine Society and the Canadian Neurosurgical Society fully endorse the recommendations of the steroid task force (see preceding paper).
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.005 |
| 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".