Longitudinal Cohort Study of Verbatim-Reported Postural Instability Symptoms as Outcomes for Online Parkinson’s Disease Trials
Bibliographic record
Abstract
BACKGROUND: The Parkinson's Disease Patient Report of Problems (PD-PROP) captures the problems and functional impact that patients report verbatim. Online research participation and advances in language analysis have enabled longitudinal collection and classification of symptoms as trial outcomes. OBJECTIVE: Analyze verbatim reports longitudinally to examine postural-instability symptoms as 1) precursors of subsequent falling and 2) newly occurring symptoms that could serve as outcome measures in randomized controlled trials. METHODS: Problems reported by >25,000 PD patients in their own words were collected online in the Fox Insight observational study and classified into symptoms by natural language processing, clinical curation, and machine learning. Symptoms of gait, balance, falling, and freezing and associated reports of having fallen in the last month were analyzed over three years of longitudinal observation by a Cox regression model in a cohort of 8,287 participants. New onset of gait, balance, falling, and freezing symptoms was analyzed by Kaplan-Meier survival techniques in 4,119 participants who had not previously reported these symptoms. RESULTS: Classified verbatim symptoms of postural instability were significant precursors of subsequent falling among participants who were older, female, and had longer PD duration. New onset of symptoms steadily increased and informed sample size estimates for clinical trials to reduce the onset of these symptoms. CONCLUSION: The tools to analyze symptoms reported by PD patients in their own words and capacity to enroll large numbers of research participants online support the feasibility and statistical power for conducting randomized clinical trials to detect effects of therapeutic interventions.
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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 | 0.019 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".