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Record W4311287829 · doi:10.1177/15269248221145032

Identifying Outcome Domains for Clinical Trials of Physical Rehabilitation Among Adults Undergoing Solid Organ Transplantation Using a Delphi Approach

2022· article· en· W4311287829 on OpenAlexaff
Tathiana Santana Shiguemoto, Tania Janaudis‐Ferreira, Neha Dewan, Sunita Mathur

Bibliographic record

VenueProgress in Transplantation · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicDelphi Technique in Research
Canadian institutionsQueen's UniversityMcGill UniversityMcGill University Health CentreUniversity of AlbertaUniversity of Toronto
Fundersnot available
KeywordsMedicineDelphi methodOutcome (game theory)Likert scaleClinical trialPhysical therapyTransplantationRehabilitationDelphiSet (abstract data type)Organ transplantationFamily medicineSurgeryPsychologyInternal medicine

Abstract

fetched live from OpenAlex

Introduction: A core outcome set (COS) improves the quality of reporting in clinical trials; however, this has not been developed for clinical trials of exercise training among adults undergoing solid organ transplant. Research Question: To explore the perspectives of transplant patients and healthcare professionals on the key outcomes domains that are relevant for clinical trials of exercise in all recipients of transplanted organs. Methods: A Delphi approach was employed with 2 rounds of online questionnaires. Participants rated the importance of outcome domains using a 9-point Likert scale ranging from “not important” to “very important”. A score of 7 to 9 (very important) by 70% or more participants and a score of 1 to 3 (not important) by less than 15% participants were required to keep an outcome domain from the first to the second round. Results: Thirty-six participants completed 2 rounds of questionnaires (90% response rate). After Round 1, 8 outcome domains were considered very important in the pretransplant phase; 16 in the early posttransplant; and 17 in the late posttransplant. Only 1 outcome domain, organ rejection in the early posttransplant phase, met the criteria to be considered very important after Round 2. Conclusion: Although consensus was not reached on the core outcome domains, this study provides preliminary information on which domains are higher priority for patients and professionals. Future work should consider a meeting with key stakeholders to allow for deeper discussion to reach consensus on a core outcome set.

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)0.019
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.291
Threshold uncertainty score0.732

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0190.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.333
GPT teacher head0.574
Teacher spread0.241 · 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 designObservational
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

Citations3
Published2022
Admission routes1
Has abstractyes

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