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Reducing Opioid Use in Patients With Chronic Pain

2023· article· en· W4385993775 on OpenAlexaboutno aff

Bibliographic record

VenueTopics in Pain Management · 2023
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineOpioidChronic painAnesthesiaPain managementIntensive care medicinePhysical therapyInternal medicine

Abstract

fetched live from OpenAlex

Can a multicomponent intervention consisting of group meetings, education, individual support, and skill-based learning reduce opioid use and improve pain interference with daily activities, compared with usual care? To answer this question, researchers in the United Kingdom and Canada enrolled 608 participants (mean age 61 years, 60% female) with chronic pain due to nonmalignant causes. Participants were recruited from 191 primary care settings in a multicenter randomized clinical trial. All patients had significant opioid use, including buprenorphine, dipipanone, morphine, diamorphine, fentanyl, hydromorphone, methadone, oxycodone, papaveretum, pentazocine, pethidine, tapentadol, and tramadol, with a daily median equivalent morphine dose of 46 mg. Patients were followed up for 1 year with a final check at 3 years. Participants were randomized 1:1 to either usual care or 3-day-long group sessions that emphasized skill-based learning and education, supplemented by one-on-one support delivered by a nurse and lay person for 12 months. The study looked at 2 main outcomes: 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). The proportion of participants who discontinued opioids at 12 months, measured by self-report. Of the 608 participants recruited, 440 (72%) completed the trial. There were no statistically significant differences 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). 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. There were no statistically significant differences in pain interference with daily life activities between the 2 groups at 12 months. 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 apparent symptoms of opioid withdrawal (shortness of breath, hot flushes, fever and pain, small intestinal bleed, and an overdose suicide attempt). The authors concluded that 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. (See Sandhu HK, Booth K, Furlan AD, et al. Reducing opioid use for chronic pain with a group-based intervention: a randomized clinical trial. JAMA. 2023;329(20):1745-1756. doi:10.1001/jama.2023.6454. 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 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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.015
GPT teacher head0.250
Teacher spread0.236 · 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 designNot applicable
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

Citations0
Published2023
Admission routes1
Has abstractyes

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