Does pain influence force steadiness? A protocol for a systematic review
Bibliographic record
Abstract
INTRODUCTION: Performing contractions with minimum force fluctuations is essential for everyday life as reduced force steadiness impacts on the precision of voluntary movements and functional ability. Several studies have investigated the effect of experimental or clinical musculoskeletal pain on force steadiness but with conflicting findings. The aim of this systematic review is to summarise the current literature to determine whether pain, whether it be clinical or experimental, influences force steadiness. METHODS AND ANALYSIS: This protocol for a systematic review was informed and reported in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols and the Cochrane Handbook for Systematic Reviews of Interventions. Key databases will be searched from inception to 31 August 2020, including MEDLINE, EMBASE, PubMed, CINAHL Plus, ZETOC and Web of Science. Grey literature and key journals will be also reviewed. Risk of bias will be assessed with the Newcastle-Ottawa tool, and the quality of the cumulative evidence assessed with the Grading of Recommendations, Assessment, Development and Evaluation guidelines. If homogeneity exists between groups of studies, meta-analysis will be conducted. Otherwise, a narrative synthesis approach and a vote-counting method will be used, while the results will be presented as net increases or decreases of force steadiness. ETHICS AND DISSEMINATION: The findings will be presented at conferences and the review will be also submitted for publication in a refereed journal. No ethical approval was required. PROSPERO REGISTRATION NUMBER: CRD42020196479.
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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 | 0.109 | 0.158 |
| Meta-epidemiology (narrow) | 0.007 | 0.007 |
| Meta-epidemiology (broad) | 0.017 | 0.015 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.087 | 0.015 |
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".