Impact of commonly administered drugs on the progression of spinal cord injury: a systematic review
Bibliographic record
Abstract
BACKGROUND: Complications arising from acute traumatic spinal cord injury (SCI) are routinely managed by various pharmacological interventions. Despite decades of clinical application, the potential impact on neurological recovery has been largely overlooked. This study aims to highlight commonly administered drugs with potential disease-modifying effects. METHODS: This systematic literature review included studies referenced in PubMed, Scopus and Web of Science from inception to March 31st, 2021, which assess disease-modifying properties on neurological and/or functional recovery of drugs routinely administered following spinal cord injury. Drug effects were classified as positive, negative, mixed, no effect, or not (statistically) reported. Risk of bias was assessed separately for animal, randomized clinical trials, and observational human studies. RESULTS: We analyzed 394 studies conducting 486 experiments that evaluated 144 unique or combinations of drugs. 195 of the 464 experiments conducted on animals (42%) and one study in humans demonstrate positive disease-modifying properties on neurological and/or functional outcomes. Methylprednisolone, melatonin, estradiol, and atorvastatin are the most common drugs associated with positive effects. Two studies on morphine and ethanol report negative effects on recovery. CONCLUSION: Despite a large heterogeneity observed in study protocols, research from bed to bench and back to bedside provides an alternative approach to identify new candidate drugs in the context of SCI. Future research in human populations is warranted to determine if introducing drugs like melatonin, estradiol, or atorvastatin would contribute to enhancing neurological outcomes after acute SCI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.010 | 0.012 |
| 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) | 0.004 | 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".