A critical appraisal of clinical practice guidelines on pharmacological treatments for spinal cord injury
Bibliographic record
Abstract
BACKGROUND CONTEXT: Spinal cord injury brings devastating consequences and huge economic burden. Different authoritative organizations have developed different guidelines for pharmacological treatments of spinal cord injury, but there is a lack of a critical appraisal of them. PURPOSE: To systematically review and appraise guidelines regarding their recommendations for pharmacological treatments for spinal cord injury. STUDY DESIGN: Systematic review. METHODS: We searched Medline, Embase, Cochrane, and Web of Science from January 2000 to January 2022 as well as guideline-specific databases (eg, Congress of Neurological Surgeons) and Google Scholar. We included the most updated guideline containing evidence-based recommendations or consensus-based recommendations developed by specific authoritative organizations if multiple versions were available. We appraised guidelines through the Appraisal of Guidelines for Research and Evaluation, 2nd edition instrument consisting of six domains (eg, applicability). With supporting evidence, recommendations were classified as: for, against, neither for nor against. We utilized an evidence assessment system to categorize the quality of supporting evidence as poor, fair, or good. RESULTS: Eight guidelines developed from 2008 to 2020 were included, but all of them scored lowest in the domain of applicability among all six domains. Twelve pharmacological agents (eg, methylprednisolone) were studied. For methylprednisolone, three guidelines (3/8=37.5%) recommended for (one evidence-based and two consensus-based), three (3/8=37.5%) recommended against (all evidence-based), and two (2/8=25%) recommended neither for nor against. For monosialotetrahexosylganglioside (GM-1), one guideline (1/4=25%) recommended for (consensus-based), one (1/4=25%) recommended against (evidence-based), and two (2/4=50%) recommended neither for nor against. For other agents (eg, minocycline), most guidelines (3/5=60%) recommended neither for nor against, one (1/5=20%) recommended against naloxone (evidence-based) and nimodipine (evidence-based), and one (1/5=20%) recommended for neural growth factor (consensus-based). The quality of most of the supporting evidence was poor, and the rest was fair. CONCLUSIONS: There were inconsistencies among recommendations for methylprednisolone and GM-1. Evidence-based recommendations tended to recommend against, whereas consensus-based recommendations tended to recommend for.
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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 | 0.017 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".