A practical approach to remote longitudinal follow‐up of Parkinson's disease: The FOUND study
Bibliographic record
Abstract
The objective of this study was to examine a remote method for maintaining long-term contact with Parkinson's disease (PD) patients participating in clinical studies. Long-term follow-up of PD patients is needed to fill critical information gaps on progression, biomarkers, and treatment. Prospective in-person assessment can be costly and may be impossible for some patients. Remote assessment using mail and telephone contact may be a practical follow-up method. Patients enrolled in the multi-center Longitudinal and Biomarker Study in Parkinson's Disease (LABS-PD) in-person follow-up study in 2006 were invited to enroll in Follow-up of Persons With Neurologic Diseases (FOUND), which is overseen by a single center under a separate, central institutional review board protocol. FOUND uses mailed questionnaires and telephone interviews to assess PD status. FOUND follow-up continued when LABS-PD in-person visits ended in 2011. Retention and agreement between remote and in-person assessments were determined. In total, 422 of 499 (84.5%) of eligible patients volunteered, AND 96% of participants were retained. Of 60 patients who withdrew consent from LABS-PD, 51 were retained in FOUND. Of 341 patients who were active in LABS-PD, 340 were retained in FOUND (99.7%) when the in-person visits ceased. Exact agreement between remote and in-person assessments was ≥ 80% for diagnosis, disease features (eg, dyskinesias), and PD medication. Correlation between expert-rated and self-reported Unified Parkinson's Disease Rating Scale and Movement Disorder Society Unified Parkinson's Disease Rating Scale, which were examined at times separated by several months, was moderate or substantial for most items. Retention was excellent using remote follow-up of research participants with PD, providing a safety net when combined with in-person visits, and also is effective as a stand-alone assessment method, providing a useful alternative when in-person evaluation is not feasible.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".