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Record W4385683574 · doi:10.1177/13524585231189674

Intermediate outcomes for clinical trials of multiple sclerosis rehabilitation interventions: Conceptual and practical considerations

2023· review· en· W4385683574 on OpenAlexaff
Marcia Finlayson, Peter Feys, Ulrik Dalgas, Daphne Kos

Bibliographic record

VenueMultiple Sclerosis Journal · 2023
Typereview
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsQueen's University
Fundersnot available
KeywordsMultiple sclerosisPsychological interventionRehabilitationPhysical medicine and rehabilitationClinical trialMedicinePhysical therapyPsychologyPsychiatryPathology

Abstract

fetched live from OpenAlex

BACKGROUND: Rehabilitation is an essential health care service and a critical component of comprehensive multiple sclerosis (MS) care. OBJECTIVE: As part of a 2-day meeting hosted by the International Advisory Committee on Clinical Trials in MS in December 2022, a panel initiated a discussion on the conceptual and practical issues related to selecting intermediate outcomes for clinical trials of MS rehabilitation interventions. RESULTS: The overarching goal of rehabilitation - optimal functioning - was acknowledged as a complex biopsychosocial phenomenon that varies with patient priorities and environmental context. This complexity means that multiple causal pathways and potential intermediate outcomes must be carefully considered during the design of clinical trials in MS rehabilitation that aim to improve functioning. In addition, practical issues must be considered such as psychometric properties of outcome measures, measure type, and characteristics of the target population, including severity of dysfunction. CONCLUSION: This article uses the International Classification of Functioning, Disability and Health as a foundation for determining relevant intermediate outcomes for clinical trials of MS rehabilitation interventions.

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)0.025
metaresearch head score (Gemma)0.469
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.866
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0250.469
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0090.006
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.002
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 head0.808
GPT teacher head0.584
Teacher spread0.224 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations5
Published2023
Admission routes1
Has abstractyes

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