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Record W4398155697 · doi:10.1136/bmjopen-2024-085272

Low-dose naltrexone for post-COVID fatigue syndrome: a study protocol for a double-blind, randomised trial in British Columbia

2024· article· en· W4398155697 on OpenAlexafffundabout
Hiten Naik, Erin Cooke, Travis Boulter, Roger Dyer, Jeffrey N. Bone, Melody Tsai, J.J. Rodríguez Cristóbal, Rhonda Jane Mckay, Xiaowei Song, Luís Nacul

Bibliographic record

VenueBMJ Open · 2024
Typearticle
Languageen
FieldMedicine
TopicFibromyalgia and Chronic Fatigue Syndrome Research
Canadian institutionsSimon Fraser UniversityFraser HealthB.C. Women's Hospital & Health CentreBC Children's HospitalWomen's Health Research InstituteUniversity of British Columbia
FundersBCCDC Foundation for Public HealthBritish Columbia Centre for Disease ControlCanadian Institutes of Health ResearchMinistry of Health, British ColumbiaMichael Smith Health Research BCHealth CanadaSt. Paul's Foundation
KeywordsMedicinePlaceboNaltrexoneChronic fatigue syndromeRandomized controlled trialClinical trialPhysical therapyInternal medicineOpioidAlternative medicinePathology

Abstract

fetched live from OpenAlex

INTRODUCTION: A significant proportion of individuals suffering from post COVID-19 condition (PCC, also known as long COVID) can present with persistent, disabling fatigue similar to myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) and post-viral fatigue syndromes. There remains no clear pharmacological therapy for patients with this subtype of PCC, which can be referred to as post-COVID fatigue syndrome (PCFS). A low dose of the opioid antagonist naltrexone (ie, low-dose naltrexone (LDN)) has emerged as an off-label treatment for treating fatigue and other symptoms in PCC. However, only small, non-controlled studies have assessed LDN in PCC, so randomised trials are urgently required. METHODS AND ANALYSIS: A prospective, randomised, double-blind, parallel arm, placebo-controlled phase II trial will be performed to assess the efficacy of LDN for improving fatigue in PCFS. The trial will be decentralised and open to eligible individuals throughout the Canadian province of British Columbia (BC). Participants will be recruited through the province-wide Post-COVID-19 Interdisciplinary Clinical Care Network (PC-ICCN) and research volunteer platform (REACH BC). Eligible participants will be 19-69 years old, have had a confirmed or physician-suspected SARS-CoV-2 infection at least 3 months prior and meet clinical criteria for PCFS adapted from the Institute of Medicine ME/CFS criteria. Individuals who are taking opioid medications, have a history of ME/CFS prior to COVID-19 or history of significant liver disease will be excluded. Participants will be randomised to an LDN intervention arm (n=80) or placebo arm (n=80). Participants in each arm will be prescribed identical capsules starting at 1 mg daily and follow a prespecified schedule for up-titration to 4.5 mg daily or the maximum tolerated dose. The trial will be conducted over 16 weeks, with assessments at baseline, 6, 12 and 16 weeks. The primary outcome will be fatigue severity at 16 weeks evaluated by the Fatigue Severity Scale. Secondary outcomes will include pain Visual Analogue Scale score, overall symptom severity as measured by the Patient Phenotyping Questionnaire Short Form, 7-day step count and health-related quality of life measured by the EuroQol 5-Dimension questionnaire. ETHICS AND DISSEMINATION: The trial has been authorised by Health Canada and approved by The University of British Columbia/Children's and Women's Health Centre of British Columbia Research Ethics Board. On completion, findings will be disseminated to patients, caregivers and clinicians through engagement activities within existing PCC and ME/CFS networks. Results will be published in academic journals and presented at conferences. TRIAL REGISTRATION NUMBER: NCT05430152.

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)0.015
metaresearch head score (Gemma)0.012
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: Protocol · Consensus signal: Protocol
Teacher disagreement score0.969
Threshold uncertainty score0.142

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.012
Meta-epidemiology (narrow)0.0040.002
Meta-epidemiology (broad)0.0080.003
Bibliometrics0.0010.002
Science and technology studies0.0030.003
Scholarly communication0.0020.002
Open science0.0030.001
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0420.008

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 head0.172
GPT teacher head0.483
Teacher spread0.312 · 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
GenreProtocol

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

Citations21
Published2024
Admission routes3
Has abstractyes

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