Glial-modulating agents for the treatment of pain: protocol for a systematic review
Bibliographic record
Abstract
Introduction Evidence suggests a role for Central nervous system glia in pain transmission and in augmenting maladaptive opioid effects. Identification of drugs that modulate glia has guided the evaluation of glial suppression as a pain management strategy. This planned systematic review will describe evidence of the efficacy and adverse effects of glial-modulating drugs in pain management. Methods and analysis A detailed search will be conducted on the Cochrane Central Register of Controlled Trials, Medline, and Embase from their inception until the date the final searches are run to identify relevant randomised controlled trials. The reference lists of retrieved studies, as well as online trial registries, will also be searched. English language, randomised, double-blind trials comparing various glial-modulating drugs with placebo and/or other comparators, with participant-reported pain assessment, will be included. Two reviewers will independently evaluate studies for eligibility, extract data and assess trial quality and potential bias. Risk of bias will be assessed using criteria outlined in the Cochrane Handbook for Systematic Review of Interventions . Primary outcomes for this review will include any validated measure of pain intensity and/or pain relief. Dichotomous data will be used to calculate risk ratio and number needed to treat or harm. The quality of evidence will be assessed using Grading of Recommendations Assessment, Development and Evaluation. Ethics and dissemination This systematic review does not require formal ethics approval. The findings will be disseminated through peer-reviewed publications and conference presentations. PROSPERO registration number CRD42021262074.
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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 | 0.054 | 0.070 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.016 | 0.016 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.112 | 0.015 |
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".