Diagnosis and coding of opioid misuse: a systematic scoping review and implementation framework
Bibliographic record
Abstract
OBJECTIVE: To evaluate current administrative coding practices for opioid misuse (OM) within the World Health Organization's International Classification of Diseases (ICD) framework and develop standardized documentation recommendations. DESIGN: Systematic scoping review following PRISMA-ScR guidelines. SETTING: Analysis of studies using administrative databases, including electronic health records (EHRs), insurance claims, and national healthcare utilization databases. SUBJECTS: Studies published in peer-reviewed journals examining administrative codes for OM, excluding those focused solely on illicit drugs, opioid use disorder (OUD), or using only natural language processing/qualitative methods. METHODS: Comprehensive search of Embase, Medline, Google Scholar, and PubMed databases following PRISMA-S extension guidelines. Three independent reviewers screened articles and extracted data. Study quality was assessed using a modified Newcastle-Ottawa Scale. RESULTS: Of 9561 initial records, 19 studies met inclusion criteria. The use of ICD-10 code F11.9* (Opioid use) emerged as the most referenced method for documenting OM, distinguishing it from OUD methods (F11.1, opioid abuse; F11.2, opioid dependence). Studies demonstrated significant heterogeneity in coding practices, resulting in code-based definitions identifying only approximately 50% of cases compared to more comprehensive clinical assessment approaches. CONCLUSIONS: While ICD-10 code F11.9* can effectively document OM as distinct from OUD, successful implementation requires consensus on the clinical definition of OM and documentation in the form of clear clinical guidelines and operationalized through enhanced EHR integration. Future research should focus on validating these approaches across diverse healthcare settings.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | medium |
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 | 0.293 | 0.406 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.015 | 0.017 |
| Bibliometrics | 0.075 | 0.043 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.009 | 0.015 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".