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Record W6963645461 · doi:10.25384/sage.c.6782829

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

2023· other· en· W6963645461 on OpenAlexaff

Bibliographic record

VenueSage Journals Data · 2023
Typeother
Languageen
Field
Topic
Canadian institutionsQueen's University
Fundersnot available
KeywordsBiopsychosocial modelInternational Classification of Functioning, Disability and HealthRehabilitationClinical trialMultiple sclerosisMEDLINEPatient-reported outcomeHealth care

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)0.033
metaresearch head score (Gemma)0.504
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.802
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0330.504
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.686
GPT teacher head0.568
Teacher spread0.118 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreDataset

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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