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Record W7161241389 · doi:10.3310/gjhs2715

Effects and costs of a group-based educational intervention to reduce opioid use in people with chronic pain: I-WOTCH RCT

2025· article· en· W7161241389 on OpenAlexaff
Harbinder Sandhu, Katie Booth, Sheeja Manchira Krishnan, Charles Abraham, Sharisse Alleyne, Shyam Balasubramanian, Lauren Betteley, Tom Bromilow, Dawn Carnes, Andrea D Furlan, S. Vijay, Kirstie L Haywood, Maddy Hill, Cynthia P Iglesias-Urrutia, Ranjit Lall, Andrea Manca, Dipesh Mistry, Joe WE Moss, Sian Newton, Vivien Nichols, Jennifer Noyes, Emma Padfield, Anisur Rahman, Kate Seers, Jane Shaw, Nicole KY Tang, Stephanie JC Taylor, Colin Tysall, Martin Underwood, Sam Eldabe

Bibliographic record

VenueHealth Technology Assessment · 2025
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsToronto Rehabilitation InstituteUniversity Health Network
FundersHealth Technology Assessment Programme
KeywordsConfidence intervalRandomized controlled trialChronic painOpioidIntervention (counseling)Clinical trialHealth carePatient education

Abstract

fetched live from OpenAlex

Background: The long-term use of strong opioids for chronic non-cancer pain puts people at risk of serious harm. Objectives: To test the effectiveness and cost-effectiveness of a multicomponent intervention targeting opioid use for the treatment of chronic pain. Design: A multicentred randomised controlled trial with embedded process evaluation. Setting: Primary care. Participants: Adults using strong opioids for non-malignant chronic. Interventions: Participants were randomised 1 : 1 (using a minimisation programme stratified by geographical locality, baseline pain intensity score and baseline morphine equivalent dose) to either usual care (an educational booklet and relaxation compact disc) or usual care plus the I-WOTCH intervention; 3 day-long group sessions delivered by a nurse and lay facilitator, plus a one-to-one session and ongoing telephone contact from the nurse to support opioid tapering. Main outcome measures: The two primary outcomes were Patient-Reported Outcomes Measurement Information System Pain Interference Short Form (8A), and proportion using no opioids, at 12 months. Results: We randomised 608 people. At 12 months, there was no between-group difference in Patient-Reported Outcomes Measurement Information System Pain Interference Short Form (8A) scores; mean difference, -0.52 (95% confidence interval -1.94 to 0.89). At 12 months, 65/225 (29%) of people in the intervention group and 15/208 (7%) of people in usual-care group reported using no opioids [odds ratio 5.55 (95% confidence interval 2.80 to 10.99)], absolute difference, 21.7% (95% confidence interval 14.8 to 28.6). Over a lifetime horizon, I-WOTCH is on average associated with an incremental cost of £9277 per person, and provides an additional 0.314 quality-adjusted life-years. The deterministic incremental cost per quality-adjusted life-year gained was £29,543. The I-WOTCH intervention may be cost-effective compared to best usual care. The process evaluation suggested group support and shared experience were important to those trying to taper. Limitations: The opioid use analysis is based solely on participant self-report. The findings only apply to people willing to consider opioid reduction and may not apply to a more complex secondary care population. The results may not be applicable to people using very high opioid doses. Conclusions: The I-WOTCH intervention helps substantially more people stop opioids than best usual care without adversely affecting pain interference. Future work: The I-WOTCH intervention should be tested in different healthcare settings and other populations. Trial registration: This trial is registered as Current Controlled Trials ISRCTN49470934. Funding: ; Vol. 30, No. 35. See the NIHR Funding and Awards website for further award information.

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 imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.017
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.007
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0020.003
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0100.001

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.

Opus teacher head0.008
GPT teacher head0.352
Teacher spread0.344 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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