A framework to standardize gait study protocols in Parkinson's disease
Bibliographic record
Abstract
BackgroundResearch over the past twenty years has shown that gait outcomes have a high sensitivity for diagnosing Parkinson's disease (PD), for detecting the effects of interventions, and for monitoring disease progression, even in early disease. However, the lack of standardization in protocols and reported gait measures is impeding data aggregation across study sites and contributes to heterogeneity in the results, thus limiting the adoption of gait outcomes in clinical trials.ObjectiveTo provide recommendations for a minimum set of gait measures to be adopted in projects evaluating people with PD to enhance standardization across the field.MethodsThe Gait Advisors Leading Outcomes for Parkinson's (GALOP) committee is an advisory committee for the MJFF. Based on a five-step approach, GALOP generated recommendations for standardizing protocols that assess quantitative gait measures, following expert consensus on best practices.ResultsBuilt on the literature and consensus amongst experts, we recommend a minimum set of meta-data to accompany gait protocols and a minimum gait assessment protocol to be performed at a comfortable speed. Suggestions on challenging testing are provided.ConclusionsTo support and empower the scientific community, we have generated recommendations to collect and share gait data gathered from people with PD using an open data repository. Standardizing gait protocols and outcomes in PD has the potential of accelerating research and clinical trials, harmonizing protocols across study sites, fostering collaborations, and in the long run, improving patient care and quality of life.
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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 | 0.831 | 0.790 |
| Meta-epidemiology (narrow) | 0.007 | 0.007 |
| Meta-epidemiology (broad) | 0.008 | 0.020 |
| Bibliometrics | 0.025 | 0.017 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.019 | 0.019 |
| Open science | 0.022 | 0.022 |
| Research integrity | 0.019 | 0.026 |
| Insufficient payload (model declined to judge) | 0.006 | 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".