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Summary of best evidence for rehabilitation management of patients with motor dysfunction after stroke.

2024· article· en· W4400800773 on OpenAlexaboutno aff
Hongyu Zhao, Luozhifei Zhou, Ling Hu, Ru Chen, Lei Dong, Qin Zhao, Lina Gong

Bibliographic record

VenuePubMed · 2024
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsnot available
Fundersnot available
KeywordsRehabilitationPhysical medicine and rehabilitationStroke (engine)Motor dysfunctionMedicinePhysical therapyPsychologyInternal medicineEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

OBJECTIVES: The rehabilitation work for patients with motor dysfunction after stroke is crucial. However, there is currently a lack of summarized evidence regarding the rehabilitation management of stroke patients in rehabilitation wards, communities, and at home. This study aims to compile relevant evidence on the rehabilitation management of patients with motor dysfunction after stroke, providing a reference for clinical and community health professionals to carry out rehabilitation interventions. METHODS: A systematic search was conducted in BMJ Best Practice, UpToDate, National Guidebook Clearinghouse, American Heart Association/American Stroke Association, Canadian Medical Association, National Institute for Health and Clinical Excellence, United States Department of Veterans Affairs/ Department of Defense, Registered Nurses Association of Ontario, JBI Evidence-Based Healthcare Center Database, The Cochrane Library, PubMed, Web of Science, Embase, CINAHL, CNKI, Wanfang Database, SinoMed, and other databases for all literature on the rehabilitation management of patients with motor dysfunction after stroke. This included clinical decision-making, guidelines, expert consensuses, recommended practices, systematic reviews, and evidence summaries, with the search period spanning from the establishment of each database to October 2023. Two researchers independently evaluated the quality of the literature. RESULTS: A total of twenty-one documents were included, consisting of 11 guidelines, 2 expert consensus, and 8 systematic reviews. Evidence was extracted and integrated from the included literature, summarizing forty-five pieces of evidence across nine areas: rehabilitation management model, rehabilitation institutions, rehabilitation teams, timing of rehabilitation interventions, rehabilitation assessment, rehabilitation programs, rehabilitation duration and frequency, rehabilitation intensity, and rehabilitation support These covered comprehensive rehabilitation management content for stroke patients in the early, subacute, and chronic phases. CONCLUSIONS: The best evidence summarized in this study for the rehabilitation management of patients with motor dysfunction after stroke is comprehensive and of high quality. It provides important guidance for clinical and community healthcare professionals in carrying out rehabilitation interventions. When applying the evidence, it is recommended to consider the current condition of the stroke patient, the extent of motor dysfunction, environmental factors, and the patient's preferences. Then, select the most appropriate rehabilitation plan, and adjust the type and intensity of training according to each patient's specific needs and preferences.

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)0.014
metaresearch head score (Gemma)0.066
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.024
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.066
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0080.009
Bibliometrics0.0240.014
Science and technology studies0.0010.001
Scholarly communication0.0060.004
Open science0.0040.003
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0130.003

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 head0.019
GPT teacher head0.256
Teacher spread0.237 · 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 designSystematic review
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

Citations4
Published2024
Admission routes1
Has abstractyes

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