Management of chronic neuropathic pain: a protocol for a multiple treatment comparison meta-analysis of randomised controlled trials
Bibliographic record
Abstract
INTRODUCTION: Chronic neuropathic pain is associated with reduced health-related quality of life and substantial socioeconomic costs. Current research addressing management of chronic neuropathic pain is limited. No review has evaluated all interventional studies for chronic neuropathic pain, which limits attempts to make inferences regarding the relative effectiveness of treatments. METHODS AND ANALYSIS: We will conduct a systematic review of all randomised controlled trials evaluating therapies for chronic neuropathic pain. We will identify eligible trials, in any language, by a systematic search of CINAHL, EMBASE, MEDLINE, AMED, HealthSTAR, DARE, PsychINFO and the Cochrane Central Registry of Controlled Trials. Eligible trials will be: (1) enrol patients presenting with chronic neuropathic pain, and (2) randomise patients to alternative interventions (pharmacological or non-pharmacological) or an intervention and a control arm. Pairs of reviewers will, independently and in duplicate, screen titles and abstracts of identified citations, review the full texts of potentially eligible trials and extract information from eligible trials. We will use a modified Cochrane instrument to evaluate risk of bias of eligible studies, recommendations from the Initiative on Methods, Measurement, and Pain Assessment in Clinical Trials (IMMPACT) to inform the outcomes we will collect, and the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system to evaluate our confidence in treatment effects. When possible, we will conduct: (1) in direct comparisons, a random-effects meta-analysis to establish the effect of reported therapies on patient-important outcomes; and (2) a multiple treatment comparison meta-analysis within a Bayesian framework to assess the relative effects of treatments. We will define a priori hypotheses to explain heterogeneity between studies, and conduct meta-regression and subgroup analyses consistent with the current best practices. ETHICS AND DISSEMINATION: We do not require ethics approval for our proposed review. We will disseminate our findings through peer-reviewed publications and conference presentations. TRIAL REGISTRATION NUMBER: PROSPERO (CRD42014009212).
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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 | 0.185 | 0.273 |
| Meta-epidemiology (narrow) | 0.013 | 0.008 |
| Meta-epidemiology (broad) | 0.035 | 0.053 |
| Bibliometrics | 0.016 | 0.018 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.011 | 0.007 |
| Research integrity | 0.013 | 0.020 |
| Insufficient payload (model declined to judge) | 0.083 | 0.011 |
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".