Inappropriate opioid prescribing practices: A narrative review
Bibliographic record
Abstract
PURPOSE: Results of a literature review to identify indicators of inappropriate opioid prescribing are presented. SUMMARY: While prescription opioids can be effective for the treatment of acute pain, inappropriate prescribing practices can increase the risk of opioid-related harms, including overdose and mortality. To date, little research has been conducted to determine how best to define inappropriate opioid prescribing. Five electronic databases were searched to identify studies (published from database inception to January 2017) that defined inappropriate opioid prescribing practices. Search terms varied slightly across databases but included opioid, analgesics, inappropriate prescribing, practice patterns, and prescription drug misuse. Gray literature and references of published literature reviews were manually searched to identify additional relevant articles. From among the 4,665 identified articles, 41 studies were selected for data extraction and analysis. Fourteen studies identified high-daily-dose opioid prescriptions, 14 studies identified coadministration of benzodiazepines and opioids, 10 studies identified inappropriate opioid prescribing in geriatric populations, 8 studies identified other patient-specific factors, 4 studies identified opioid prescribing for the wrong indication, and 4 studies identified factors such as initiation of long-acting opioids in opioid-naive patients as indicators of inappropriate opioid prescribing. CONCLUSION: A literature review identified various indicators of inappropriate opioid prescribing, including the prescribing of high daily doses of opioids, concurrent benzodiazepine administration, and geriatric-related indicators. Given the significant contribution of inappropriate opioid prescribing to opioid-related harms, identification of these criteria is important to inform and improve opioid prescribing practices among healthcare providers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.017 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".