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Record W2735474392 · doi:10.1177/1747493017711816

Agreed definitions and a shared vision for new standards in stroke recovery research: The Stroke Recovery and Rehabilitation Roundtable taskforce

2017· article· en· W2735474392 on OpenAlexafffund
Julie Bernhardt, Kathryn S. Hayward, Gert Kwakkel, Nick Ward, Steven L. Wolf, Karen Borschmann, John W. Krakauer, Lara A. Boyd, S. Thomas Carmichael, Dale Corbett, Steven C. Cramer

Bibliographic record

VenueInternational Journal of Stroke · 2017
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsHeart and Stroke FoundationUniversity of OttawaVancouver Coastal HealthUniversity of British Columbia
FundersNational Institute of Neurological Disorders and StrokeBiotechnology and Biological Sciences Research CouncilNational Institutes of HealthNational Health and Medical Research CouncilState Government of VictoriaIpsenMichael Smith Health Research BCEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentCanada Research ChairsHeart and Stroke Foundation of Canada
KeywordsMedicineStroke (engine)RehabilitationPhysical medicine and rehabilitationStroke recoveryPhysical therapyIntervention (counseling)Clinical trialNursingPathology

Abstract

fetched live from OpenAlex

The first Stroke Recovery and Rehabilitation Roundtable established a game changing set of new standards for stroke recovery research. Common language and definitions were required to develop an agreed framework spanning the four working groups: translation of basic science, biomarkers of stroke recovery, measurement in clinical trials and intervention development and reporting. This paper outlines the working definitions established by our group and an agreed vision for accelerating progress in stroke recovery research.

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)0.719
metaresearch head score (Gemma)0.582
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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.281
Threshold uncertainty score0.347

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.7190.582
Meta-epidemiology (narrow)0.0040.007
Meta-epidemiology (broad)0.0110.012
Bibliometrics0.0170.011
Science and technology studies0.0200.042
Scholarly communication0.0520.046
Open science0.0340.062
Research integrity0.0400.108
Insufficient payload (model declined to judge)0.0040.004

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 head0.083
GPT teacher head0.405
Teacher spread0.322 · 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

Citations1,113
Published2017
Admission routes2
Has abstractyes

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