Physical therapy for the management of motor symptoms in amyotrophic lateral sclerosis: protocol for a systematic review
Bibliographic record
Abstract
INTRODUCTION: The prescription of an intervention plan can be challenging for the physical therapist, considering clinical phenotypes, individual prognosis and the rapid, progressive and deteriorating nature of amyotrophic lateral sclerosis (ALS). In this context, therapeutic exercises (eg, resistance and aerobic exercises) for patients with ALS remain controversial and may influence the treatment plan. Therefore, this review aims to critically assess whether physical therapy interventions are effective for improving functional capacity, quality of life and fatigue of individuals with ALS. METHODS AND ANALYSIS: Studies will be selected according to eligibility criteria, and language, geographical area or publication date will not be restricted. Four databases will be used: MEDLINE, EMBASE, Cochrane Library (CENTRAL) and Physiotherapy Evidence Database (PEDro). Searches will also be conducted on ClinicalTrials.gov and references from included studies. We plan to conduct the searches between October and December 2022. Two independent authors will examine titles and abstracts and exclude irrelevant studies and duplicates. We will assess the quality of studies and quality of evidence, and disagreements will be resolved with a third researcher. The findings will be presented in the text and tables; if possible, we will perform meta-analyses. ETHICS AND DISSEMINATION: No ethical approval is required because this study does not involve human beings. We will publish our findings in peer-reviewed journals. PROSPERO REGISTRATION NUMBER: CRD42021251350.
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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 | 0.066 | 0.062 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.015 | 0.013 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.061 | 0.009 |
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".