Rehabilitation for balance impairment in patients after stroke: a protocol of a systematic review and network meta-analysis
Bibliographic record
Abstract
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).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.070 | 0.083 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.024 | 0.031 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.043 | 0.004 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".