Methylprednisolone for the Treatment of Patients with Acute Spinal Cord Injuries: A Propensity Score-Matched Cohort Study from a Canadian Multi-Center Spinal Cord Injury Registry
Bibliographic record
Abstract
In prior analyses of the effectiveness of methylprednisolone for the treatment of patients with acute traumatic spinal cord injuries (TSCIs), the prognostic importance of patients ’ neurological levels of injury and their baseline severity of impairment has not been considered. Our objective was to determine whether methylprednisolone improved motor recovery among participants in the Rick Hansen Spinal Cord Injury Registry (RHSCIR). We identified RHSCIR participants who received methylprednisolone according to the Second National Spinal Cord Injury Study (NASCIS-II) protocol and used propensity score matching to account for age, sex, time of neurological exam, varying neurological level of injury, and baseline severity of neurological impairment. We compared changes in total, upper extremity, and lower extremity motor scores using the Wilcoxon signed-rank test and performed sensitivity analyses using negative binomial regression. Forty-six patients received methylprednisolone and 1555 received no steroid treatment. There were no significant dif-ferences between matched participants for each of total (13.7 vs. 14.1, respectively; p = 0.43), upper extremity (7.3 vs. 6.4; p = 0.38), and lower extremity (6.5 vs. 7.7; p = 0.40) motor recovery. This result was confirmed using a multivariate model and, as predicted, only cervical (C1–T1) rather than thoracolumbar (T2–L3) injury levels ( p < 0.01) and reduced baseline injury severity (American Spinal Injury Association [ASIA] Impairment Scale grades; p < 0.01) were associated with
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".