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Record W2963973984 · doi:10.1136/bmjopen-2018-026844

Rehabilitation for balance impairment in patients after stroke: a protocol of a systematic review and network meta-analysis

2019· review· en· W2963973984 on OpenAlexaboutno aff
Juan Li, Dongling Zhong, Jing Ye, Mingxing He, Xicen Liu, Hui Zheng, Rongjiang Jin, Shaolan Zhang

Bibliographic record

VenueBMJ Open · 2019
Typereview
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsnot available
FundersNational Natural Science Foundation of ChinaCardiff University
KeywordsMedicineRehabilitationMeta-analysisProtocol (science)Stroke (engine)Physical medicine and rehabilitationBalance (ability)Systematic reviewPhysical therapyMEDLINEAlternative medicinePathology

Abstract

fetched live from OpenAlex

INTRODUCTION: Multiple rehabilitation therapies have been reported to be effective for poststroke balance impairment. However, the comparative effectiveness of these rehabilitation therapies is still unclear. Therefore, the aim of this study is to summarise evidence and identify the most effective rehabilitation therapy for poststroke balance impairment. METHODS AND ANALYSIS: The following databases will be searched: China Biology Medicine, China National Knowledge Infrastructure, Wan Fang Data, the Chinese Science and Technology Periodical Database, Medline, Excerpt Medical Database (EMBASE), Web of Science, the Cochrane Library, from inception to June 2019. All randomised controlled trials that have used rehabilitation interventions to treat poststroke balance impairment will be included. The primary outcomes are the Berg Balance Scale, the Fugl-Meyer Assessment (balance), the Postural Assessment Scale for Stroke, as well as the function in sitting test, the Sitting Balance Scale, the Ottawa Sitting Scale, the Activities-specific Balance Confidence Scale, the Overall Balance Index and the Brunel Balance Assessment. The secondary outcomes include the Barthel Index, the Functional Ambulation Category Scale, fall rates, the Timed Up and Go test, the MOS 36-Item Short-Form Health Survey, and adverse events. To ensure that all relevant studies are included without personal bias, study selection, data extraction and quality assessment will be performed independently by two reviewers. Risk of bias will be assessed with the Cochrane risk of bias assessment tool. Review Manager V.5.3 software will be used to make bias risk diagram and pairwise meta-analysis, while network data synthesis will be performed using WinBUGS V.1.4.3 and R software. ETHICS AND DISSEMINATION: Ethics approval is not required for systematic review and network meta-analysis. The results will be submitted to a peer review journal or at a conference. TRIAL REGISTRATION NUMBER: PROSPERO (CRD 42018107441).

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)0.070
metaresearch head score (Gemma)0.083
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: Protocol · Consensus signal: Protocol
Teacher disagreement score0.070
Threshold uncertainty score0.368

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0700.083
Meta-epidemiology (narrow)0.0060.004
Meta-epidemiology (broad)0.0240.031
Bibliometrics0.0120.011
Science and technology studies0.0030.003
Scholarly communication0.0060.005
Open science0.0050.004
Research integrity0.0060.005
Insufficient payload (model declined to judge)0.0430.004

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 head0.133
GPT teacher head0.468
Teacher spread0.336 · 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
GenreProtocol

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

Citations98
Published2019
Admission routes1
Has abstractyes

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