Risk of infections among persons treated with opioids for chronic pain: a systematic review and meta-analysis protocol
Bibliographic record
Abstract
INTRODUCTION: Millions of persons with chronic pain across North America and Europe use opioids. While the immunosuppressive properties of opioids are associated with risks of infections, these outcomes could be mitigated through careful patient selection and monitoring practices when appropriate. It is important to recognise that some patients do benefit from a carefully tailored opioid therapy. Enough primary studies have been published to date regarding the role of opioids in potential immunosuppression presenting as an increased rate of infection acquisition, infectious complications and mortality. There is thus a critical need for a consensus in this area. METHODS AND ANALYSIS: The methodology is based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement, the MOOSE Guidelines for Meta-Analyses and Systematic Reviews of Observational Studies and the Cochrane Handbook for Systematic Reviews of Interventions. We plan to systematically search Ovid MEDLINE, CINAHL, PsycINFO, EMB Review, EMBASE, Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials and Google Scholar databases from their inception date to December 2023. Full-text primary studies that report measurable outcomes in adults with chronic pain, all routes of opioid use, all types of infections and all settings will be included. We will identify a scope of reported infections and the evidence on the association of opioid use (including specific opioid, dosage, formulation and duration of use) with the risk of negative infectious outcomes. Opioid use-associated outcomes, comparing opioid use with another opioid or a non-opioid medication, will be reported. The meta-analysis will incorporate individual risk factors. If data are insufficient, the results will be synthesised narratively. Publication bias and confounding evaluation will be performed. The Grading of Recommendations Assessment, Development and Evaluation framework will be used. ETHICS AND DISSEMINATION: Approval for the use of published data is not required. The results will be published, presented at conferences and discussed in deliberative dialogue groups. PROSPERO REGISTRATION NUMBER: CRD42023402812.
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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 | 0.049 | 0.059 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.021 | 0.035 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.046 | 0.004 |
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".