Risk factors for addiction among patients receiving prescribed opioids: a systematic review protocol
Bibliographic record
Abstract
BACKGROUND: Opioid addiction prevention has become an urgent public health priority, with several countries declaring a state of emergency due to rising death tolls from opioid abuse. Reducing the risk of developing addiction among opioid-naïve patients exposed to prescribed opioids during the process of medical care may be an important primary prevention strategy. Our objective is to synthesize the available evidence about factors associated with the development of addiction among patients first exposed to prescribed opioids, with a focus on opioid-naïve patients. METHODS: We will perform a systematic search of MEDLINE, Embase, Cochrane Central Register of Controlled Trials, and other databases in collaboration with a health information specialist using a comprehensive search strategy. We will also supplement our search with a scan of the grey literature to identify relevant ongoing and unpublished studies. We will include studies reporting on risk factors for opioid addiction in patients prescribed opioid analgesic therapy through a prescription from a licensed medical professional, with a focus on opioid-naïve patients. We will exclude studies focusing on patients who are first exposed to illicit opioids, those who use prescription opioids for cancer pain, and/or who are palliative. Two reviewers will independently review titles, abstracts, and full texts for inclusion and exclusion criteria. They will then extract data from included full texts using standardized piloted data extraction forms and assess study quality through risk of bias assessment. We will synthesize the effect sizes of risk factors derived from clinically homogenous studies with similar designs and the remaining ones qualitatively. DISCUSSION: Understanding risk factors for opioid addiction among patients who require analgesia has the potential to inform clinical care and opioid prescribing guidelines aiming to reduce opioid addiction. We will also use this information as a starting point for developing interventions for primary prevention.
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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 | 0.068 | 0.070 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.021 | 0.015 |
| Bibliometrics | 0.017 | 0.013 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.057 | 0.006 |
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".