1045Patterns, predictors and outcomes of opioid use in Australia: evidence for an epidemic?
Bibliographic record
Abstract
Abstract Focus and outcomes for participants This symposium will focus on evidence from pharmacoepidemiological research on prevalence and incidence of prescription opioid prevalence, opioid utilisation patterns and related harms in Australia. The symposium will also discuss interventions to reduce opioid-related harm. The speakers will discuss how opioid use and prescribing culture has evolved over the last two decades and provide insight from recent research using big data analysis on prescription opioid use and related outcomes. Rationale for the symposium, including for its inclusion in the Congress In 2016, there were 679 overdose deaths involving opioid pain medications in Australia, with the majority of these deaths unintentional. There is growing concern that harm from opioid pain medications in Australia may mimic the situation in the United States and Canada, where the problem has been labelled an epidemic. Recent Monash led research using Australia’s Pharmaceutical Benefits Scheme data for 2013 to 2018 found that approximately 3 million Australians adults use opioids each year and approximately 1.9 million adults start taking opioids. Of this population of adults that start using opioids, 2.6% become long-term users for over a year. Long-term use and the use of strong opioids are associated with a range of adverse health outcomes. High-dose opioid use has also been associated with falls, fractures, hospitalisations and motor vehicle injuries. The rationale of this symposium is to draw on the expertise of the presenters and share innovative epidemiological and data analysis methods to understand opioid use in the Australian context. The creation of such a forum at the World Congress will allow for enhanced knowledge sharing on both a national and international platform and assist in planning strategies to better anticipate and manage potential harms when opioid pain medications are initiated. Presentation program The Symposium consists of four presentations: Names of presenters Names of facilitator or chair Professor Danny Liew, Deputy Head of School, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia Ms. Michelle Steeper, Research Officer, Centre for Medicine Use and Safety, Monash University, Melbourne, Australia
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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 | 0.015 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 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".