Opioids for chronic non-cancer pain: a protocol for a systematic review of randomized controlled trials
Bibliographic record
Abstract
BACKGROUND: Opioids are prescribed frequently and increasingly for the management of chronic non-cancer pain (CNCP). Current systematic reviews have a number of limitations, leaving uncertainty with regard to the benefits and harms associated with opioid therapy for CNCP. We propose to conduct a systematic review and meta-analysis to summarize the evidence for using opioids in the treatment of CNCP and the risk of associated adverse events. METHODS AND DESIGN: Eligible trials will include those that randomly allocate patients with CNCP to treatment with any opioid or any non-opioid control group. We will use the guidelines published by the Initiative on Methods, Measurement, and Pain Assessment in Clinical Trials (IMMPACT) to inform the outcomes that we collect and present. We will use the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) system to evaluate confidence in the evidence on an outcome-by-outcome basis. Teams of reviewers will independently and in duplicate assess trial eligibility, abstract data, and assess risk of bias among eligible trials. To ensure interpretability of our results, we will present risk differences and measures of relative effect for all outcomes reported and these will be based on anchor-based minimally important clinical differences, when available. We will conduct a priori defined subgroup analyses consistent with current best practices. DISCUSSION: Our review will evaluate both the effectiveness and the adverse events associated with opioid use for CNCP, evaluate confidence in the evidence using the GRADE approach, and prioritize patient-important outcomes with a focus on functional gains guided by IMMPACT recommendations. Our results will facilitate evidence-based management of patients with CNCP and identify key areas for future research. TRIAL REGISTRATION: Our protocol is registered on PROSPERO (CRD42012003023), http://www.crd.york.ac.uk/PROSPERO.
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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 | 0.158 | 0.201 |
| Meta-epidemiology (narrow) | 0.008 | 0.007 |
| Meta-epidemiology (broad) | 0.020 | 0.022 |
| Bibliometrics | 0.014 | 0.019 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.072 | 0.014 |
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".