The effect of dyskinesia on postural control, balance, gait, and fall risk in people with Parkinson’s disease: a systematic review protocol
Bibliographic record
Abstract
Dyskinesia affects the limbs, trunk, and head and is more prevalent in people with Parkinson’s disease (PD) and a history of falls. More evidence about the effects of dyskinesia on postural control, balance, gait, and fall risk could help improve the quality of life of individuals with PD. This review aims to examine associations between dyskinesia and postural control, balance, gait, and fall risk in individuals with PD. Such information could lead to new approaches to quality of life improvement among individuals with PD. PubMed, CINAHL, PsycInfo, Scopus, and SciELO will be searched for longitudinal, cohort, and case-control studies published in English or Portuguese in any year that investigated the association between dyskinesia and postural control, balance, gait, and fall risk in individuals with PD. Two reviewers will independently evaluate the titles, abstracts, and full texts according to PRISMA guidelines to select eligible studies for the review. Data on participants, dyskinesia, postural control, balance, gait, and fall risk will be extracted and summarized in tables. Two reviewers will independently assess the methodological quality of each study using the Newcastle Ottawa quality assessment scale. Meta-analysis will not be performed. The results of this systematic review will offer insight into the effects of dyskinesia on postural control, balance, gait, and fall risk. Such information could significantly contribute to informed decisions about early motor intervention in individuals with PD.
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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 | 0.055 | 0.065 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.023 | 0.020 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.048 | 0.006 |
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".