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International consensus recommendations for outcome measurement in post-stroke arm rehabilitation trials

2021· article· en· W3102114947 on OpenAlexaboutno aff
Julie Duncan Millar, Frederike van Wijck, Alex Pollock, Myzoon Ali

Bibliographic record

VenueEuropean Journal of Physical and Rehabilitation Medicine · 2021
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsnot available
FundersGlasgow Caledonian University
KeywordsMedicineRehabilitationInternational Classification of Functioning, Disability and HealthDelphi methodPhysical therapyRandomized controlled trialPhysical medicine and rehabilitationStroke (engine)Psychological interventionPopulationDelphiOccupational therapyMEDLINENursing

Abstract

fetched live from OpenAlex

BACKGROUND: Existing randomized controlled trials (RCTs) of arm rehabilitation interventions after stroke use a wide range of outcome measures, limiting ability to pool data to determine efficacy. Published recommendations also lack stroke survivor, carer and clinician involvement specifically about perceived relevance and importance of outcomes and measures. AIM: To generate international consensus recommendations for selection of outcome measures for use in future stroke RCTs in arm rehabilitation, considering outcomes important to stroke survivors, carers and clinicians. The recommendations are the Standardizing Measurement in Arm Rehabilitation Trials (SMART) Toolbox. DESIGN: Two-round international e-Delphi Survey and consensus meeting. SETTING: Online and University. POPULATION: Fifty-five researchers and clinicians with expertise in stroke upper limb rehabilitation from 18 countries (e-Delphi); N.=13 researchers and clinicians, N.=2 stroke survivors, N.=1 carer (consensus meeting). METHODS: Using systematically identified outcome measures from published RCTs, we conducted a two-round international e-Delphi Survey with researchers and clinicians to identify the most important measures for inclusion in the toolbox. Measures that achieved ≥60% consensus were categorized using the International Classification of Functioning, Disability and Health Framework (ICF); psychometric properties were ascertained from literature and research resources. At a final consensus meeting, expert stakeholders selected measures for inclusion in the toolbox. RESULTS: e-Delphi participants recommended 28/170 measures for discussion at the final consensus meeting. Expert stakeholders (N.=16) selected the Visual Analogue Scale for pain/0-10 Numeric Pain Rating Scale, dynamometry, Action Research Arm Test, Wolf Motor Function Test, Barthel Index, Motricity Index and Fugl-Meyer Assessment (upper limb section of each), Box and Block Test, Motor Activity Log 14, Nine Hole Peg Test, Functional Independence Measure, EQ-5D, Canadian Occupational Performance Measure and Modified Rankin Scale for inclusion in the toolbox. CONCLUSIONS: The SMART Toolbox provides a refined selection of measures that capture outcomes considered important by stakeholders for each ICF domain. CLINICAL REHABILITATION IMPACT: The toolbox will facilitate data aggregation for efficacy analyses thereby strengthening evidence to inform clinical practice. Clinicians can also use the toolbox to guide selection of measures ensuring a patient-centered focus.

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)0.597
metaresearch head score (Gemma)0.744
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: Methods
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.403
Threshold uncertainty score0.496

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.5970.744
Meta-epidemiology (narrow)0.0070.008
Meta-epidemiology (broad)0.0160.041
Bibliometrics0.0320.024
Science and technology studies0.0080.013
Scholarly communication0.0230.016
Open science0.0290.022
Research integrity0.0460.041
Insufficient payload (model declined to judge)0.0170.020

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 head0.071
GPT teacher head0.363
Teacher spread0.292 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
DomainMethods
GenreMethods

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

Citations17
Published2021
Admission routes1
Has abstractyes

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