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Record W2914926515 · doi:10.1002/9781118492390.ch18

Rehabilitation after stroke: evidence, practice, and new directions

2019· other· en· W2914926515 on OpenAlexaff
Coralie English, Audrey Bowen, Debbie Hébert, Julie Bernhardt

Bibliographic record

Venuenot available
Typeother
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsToronto Rehabilitation InstituteUniversity of Toronto
Fundersnot available
KeywordsRehabilitationStroke (engine)Physical medicine and rehabilitationPsychological interventionProcess (computing)Quality (philosophy)Physical therapyPsychologyMedicineNursingComputer scienceEngineering

Abstract

fetched live from OpenAlex

The process of rehabilitation involves harnessing and optimizing the recovery processes, to enable people with stroke to lead active, independent lives with the best possible quality of life. Stroke recovery is an ongoing process that may continue for months or years. This chapter draws on the most recent international Clinical Guidelines for stroke rehabilitation to present an overview of the current evidence for effective rehabilitation interventions. We highlight a few key areas, focusing on physical rehabilitation, and present aspirational vision for the future of recovery and rehabilitation after stroke.

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)0.026
metaresearch head score (Gemma)0.081
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.026
Threshold uncertainty score0.139

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.081
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0080.009
Science and technology studies0.0010.003
Scholarly communication0.0080.008
Open science0.0020.003
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0160.004

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 head0.015
GPT teacher head0.313
Teacher spread0.298 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations10
Published2019
Admission routes1
Has abstractyes

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