Persistent opioid use after hospital discharge in Australia: a systematic review
Bibliographic record
Abstract
Objective This systematic review identified studies that provided an estimate of persistent opioid use following patient discharge from hospital settings in Australia. Methods A literature search was performed on 5 December 2020, with no date restrictions to identify studies that reported a rate of persistent opioid use following patient discharge from Australian Hospitals. The search strategy combined all terms relating to the themes 'hospital patients', 'prescribing', 'opioids' and 'Australia'. Studies that dealt solely with cancer, palliative care or addiction medicine were excluded. The databases searched in this review were Embase, PubMed, Scopus, CINAHL, and International Pharmaceutical Abstracts. Studies were assessed for bias using the Newcastle-Ottawa Scale and considered against international literature. Results In total, 13 publications are included for final analysis in this review. Of these, 11 articles relate to post-surgical opioid use. With one exception, studies were of a 'good' quality. Methods of data collection in included studies were a mixture of those conducting follow up of patients directly over time and those utilising dispensing databases. Persistent opioid use among surgical patients generally ranged from 3.9 to 10.5% at between 2 and 4 months after discharge. Conclusions How rates of persistent opioid use following hospital encounters in Australia are established, and how long after discharge rates are reported, is heterogeneous. Literature primarily relates to post-surgical patients, with very few studies investigating other settings such as encounters with the emergency department.
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".