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Record W4313144471 · doi:10.1136/bmjopen-2021-059635

Automated digital counselling with social network support as a novel intervention for patients with heart failure: protocol for randomised controlled trial

2022· article· en· W4313144471 on OpenAlexafffundabout
Rachel Peiris, Heather J. Ross, Christopher T. Chan, Stephanie Poon, Bourne L. Auguste, Valeria E. Rac, Michael E. Farkouh, Michael McDonald, Janusz Kaczorowski, Jillianne Code, Juan Duero Posada, Stephanie L. Ong, Jeremy Kobulnik, George Tomlinson, Ella Huszti, JoAnne Arcand, Scott Thomas, Ayub Akbari, Robert Maunder, Steven A. Grover, Emily Seto, Anne Simard, Brad Pope, Marc Bains, Carmen McIntyre, Chris Torbay, Fatima Syed, Robert P. Nolan

Bibliographic record

VenueBMJ Open · 2022
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsMcGill UniversityUniversité de MontréalUniversity of British ColumbiaTed Rogers Centre for Heart ResearchPublic Health OntarioOntario Tech UniversitySinai Health SystemSunnybrook Health Science CentreOttawa HospitalHealth Sciences CentreUniversity Health NetworkUniversity of TorontoInstitute of Health Services and Policy Research
FundersCanadian Institutes of Health Research
KeywordsMedicineProtocol (science)Intervention (counseling)Randomized controlled trialHeart failureAlternative medicinePhysical therapyFamily medicineNursingInternal medicinePathology

Abstract

fetched live from OpenAlex

INTRODUCTION: Heart failure (HF) symptoms improve through self-care, for which adherence remains low among patients despite the provision of education for these behaviours by clinical teams. Open Access Digital Community Promoting Self-Care, Peer Support and Health Literacy (ODYSSEE-vCHAT) combines automated digital counselling with social network support to improve mortality and morbidity, engagement with self-care materials, and health-related quality of life. METHODS AND ANALYSIS: Use of ODYSSEE-vCHAT via Internet-connected personal computer by 162 HF patients will be compared with a control condition over 22 months. The primary outcome is a composite index score of all-cause mortality, all-cause emergency department visits, and HF-related hospitalisation at trial completion. Secondary outcomes include individual components of the composite index, engagement with self-care materials, and patient-reported measures of physical and psychosocial well-being, disease management, health literacy, and substance use. Patients are recruited from tertiary care hospitals in Toronto, Canada and randomised on a 1:1 ratio to both arms of the trial. Online assessments occur at baseline (t=0), months 4, 8 and 12, and trial completion. Ordinal logistic regression analyses and generalised linear models will evaluate primary and secondary outcomes. ETHICS AND DISSEMINATION: The trial has been approved by the research ethics boards at the University Health Network (20-5960), Sunnybrook Hospital (5117), and Mount Sinai Hospital (21-022-E). Informed consent of eligible patients occurs in person or online. Findings will be shared with key stakeholders and the public. Results will allow for the preparation of a Canada-wide phase III trial to evaluate the efficacy of ODYSSEE-vCHAT in improving clinical outcomes and raising the standard of outpatient care. TRIAL REGISTRATION NUMBER: NCT04966104.

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)0.028
metaresearch head score (Gemma)0.026
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 score0.102
Threshold uncertainty score0.342

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0280.026
Meta-epidemiology (narrow)0.0070.003
Meta-epidemiology (broad)0.0150.007
Bibliometrics0.0030.004
Science and technology studies0.0030.004
Scholarly communication0.0060.004
Open science0.0040.003
Research integrity0.0080.010
Insufficient payload (model declined to judge)0.1020.018

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 head0.076
GPT teacher head0.489
Teacher spread0.413 · 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

Citations4
Published2022
Admission routes3
Has abstractyes

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