Assessment tools for problematic opioid use in palliative care: A scoping review
Bibliographic record
Abstract
Background: Screening for problematic opioid use is increasingly recommended in patients receiving palliative care. Aim: To identify tools used to assess for the presence or risk of problematic opioid use in palliative care. Design: Scoping review. Data sources: Bibliographic databases (inception to January 31, 2020), reference lists, and grey literature were searched to find primary studies reporting on adults receiving palliative care and prescription opioids to manage symptoms from advanced cancer, neurodegenerative diseases, or end-stage organ diseases; and included tools to assess for problematic opioid use. There were no restrictions based on study design, location, or language. Results: We identified 42 observational studies (total 14,431 participants) published between 2009 and 2020 that used questionnaires ( n = 32) and urine drug tests ( n = 21) to assess for problematic opioid use in palliative care, primarily in US ( n = 38) and outpatient palliative care settings ( n = 36). The questionnaires were Cut down, Annoyed, Guilty, and Eye-opener (CAGE, n = 8), CAGE-Adapted to Include Drugs (CAGE-AID, n = 6), Opioid Risk Tool ( n = 9), Screener and Opioid Assessment for Patients with Pain (SOAPP; n = 3), SOAPP-Revised ( n = 2), and SOAPP-Short Form ( n = 5). Only two studies’ primary objectives were to evaluate a questionnaire’s psychometric properties in patients receiving palliative care. There was wide variation in how urine drug tests were incorporated into palliative care; frequency of abnormal urine drug test results ranged from 8.6% to 70%. Conclusion: Given the dearth of studies using tools developed or validated specifically for patients receiving palliative care, further research is needed to inform clinical practice and policy regarding problematic opioid use in palliative care.
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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 | 0.037 | 0.168 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.032 | 0.029 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| 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, 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".