Reducing opioid use for chronic non-cancer pain in primary care using an evidence-based, theory-informed, multistrategic, multistakeholder approach: a single-arm time series with segmented regression
Bibliographic record
Abstract
BACKGROUND: Many countries have high opioid use among people with chronic non-cancer pain. Knowledge about effective interventions that could be implemented at scale is limited. We designed a national intervention that included audit and feedback, deprescribing guidance, information on catastrophising assessment, pain neuroscience education and a cognitive tool for use by patients with their healthcare providers. METHOD: We used a single-arm time series with segmented regression to assess rates of people using opioids before (January 2015 to September 2017), at the time of (October 2017) and after the intervention (November 2017 to August 2019). We used a cohort with historical comparison group and log binomial regression to examine the rate of psychologist claims in opioid users not using psychologist services prior to the intervention. RESULTS: 13 968 patients using opioids, 8568 general practitioners, 8370 pharmacies and accredited pharmacists and 689 psychologists were targeted. The estimated difference in opioid use was -0.51 persons per 1000 persons per month (95% CI -0.69, -0.34; p<0.001) as a result of the intervention, equating to 25 387 (95% CI 24 676, 26 131) patient-months of opioid use avoided during the 22-month follow-up. The targeted group had a significantly higher rate of incident patient psychologist claims compared with the historical comparison group (rate ratio: 1.37, 95% CI 1.16, 1.63; p<0.001), equating to an additional 690 (95% CI 289, 1167) patient-months of psychologist treatment during the 22-month follow-up. CONCLUSIONS: Our intervention addressed the cognitive, affective and sensory factors that contribute to pain and consequent opioid use, demonstrating it could be implemented at scale and was associated with a reduction in opioid use and increasing utilisation of psychologist services.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".