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Record W4377564077 · doi:10.1001/jama.2023.6454

Reducing Opioid Use for Chronic Pain With a Group-Based Intervention

2023· article· en· W4377564077 on OpenAlexaff
Harbinder Sandhu, Katie Booth, Andrea D Furlan, Jane Shaw, Dawn Carnes, Stephanie Taylor, Charles Abraham, Sharisse Alleyne, Shyam Balasubramanian, Lauren Betteley, Kirstie Haywood, Cynthia P Iglesias-Urrutia, Sheeja Krishnan, Ranjit Lall, Andrea Manca, Dipesh Mistry, Sian Newton, Jennifer Noyes, Vivien Nichols, Emma Padfield, Anisur Rahman, Kate Seers, Nicole K. Y. Tang, Colin Tysall, Sam Eldabe, Martin Underwood

Bibliographic record

VenueJAMA · 2023
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsInstitute for Work & HealthToronto Rehabilitation InstituteUniversity of TorontoUniversity Health Network
FundersNational Institute for Health and Care Research
KeywordsMedicineChronic painOpioidIntervention (counseling)Physical therapyInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

Importance: Opioid use for chronic nonmalignant pain can be harmful. Objective: To test whether a multicomponent, group-based, self-management intervention reduced opioid use and improved pain-related disability compared with usual care. Design, Setting, and Participants: Multicentered, randomized clinical trial of 608 adults taking strong opioids (buprenorphine, dipipanone, morphine, diamorphine, fentanyl, hydromorphone, methadone, oxycodone, papaveretum, pentazocine, pethidine, tapentadol, and tramadol) to treat chronic nonmalignant pain. The study was conducted in 191 primary care centers in England between May 17, 2017, and January 30, 2019. Final follow-up occurred March 18, 2020. Intervention: Participants were randomized 1:1 to either usual care or 3-day-long group sessions that emphasized skill-based learning and education, supplemented by 1-on-1 support delivered by a nurse and lay person for 12 months. Main Outcomes and Measures: The 2 primary outcomes were Patient-Reported Outcomes Measurement Information System Pain Interference Short Form 8a (PROMIS-PI-SF-8a) score (T-score range, 40.7-77; 77 indicates worst pain interference; minimal clinically important difference, 3.5) and the proportion of participants who discontinued opioids at 12 months, measured by self-report. Results: Of 608 participants randomized (mean age, 61 years; 362 female [60%]; median daily morphine equivalent dose, 46 mg [IQR, 25 to 79]), 440 (72%) completed 12-month follow-up. There was no statistically significant difference in PROMIS-PI-SF-8a scores between the 2 groups at 12-month follow-up (-4.1 in the intervention and -3.17 in the usual care groups; between-group difference: mean difference, -0.52 [95% CI, -1.94 to 0.89]; P = .15). At 12 months, opioid discontinuation occurred in 65 of 225 participants (29%) in the intervention group and 15 of 208 participants (7%) in the usual care group (odds ratio, 5.55 [95% CI, 2.80 to 10.99]; absolute difference, 21.7% [95% CI, 14.8% to 28.6%]; P < .001). Serious adverse events occurred in 8% (25/305) of the participants in the intervention group and 5% (16/303) of the participants in the usual care group. The most common serious adverse events were gastrointestinal (2% in the intervention group and 0% in the usual care group) and locomotor/musculoskeletal (2% in the intervention group and 1% in the usual care group). Four people (1%) in the intervention group received additional medical care for possible or probable symptoms of opioid withdrawal (shortness of breath, hot flushes, fever and pain, small intestinal bleed, and an overdose suicide attempt). Conclusions and Relevance: In people with chronic pain due to nonmalignant causes, compared with usual care, a group-based educational intervention that included group and individual support and skill-based learning significantly reduced patient-reported use of opioids, but had no effect on perceived pain interference with daily life activities. Trial Registration: isrctn.org Identifier: ISRCTN49470934.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.842
Threshold uncertainty score0.361

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.286
Teacher spread0.265 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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".

Quick stats

Citations62
Published2023
Admission routes1
Has abstractyes

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