Outcome measures for clinical trials in Parkinson’s disease: achievements and shortcomings
Bibliographic record
Abstract
Three areas of intense investigation in Parkinson's disease clinical trials include symptomatic treatment of Parkinsonism, disease-modifying therapy (or neuroprotection), and the prevention and treatment of motor complications of dopaminergic therapy. Difficulty interpreting the results of many studies in recent years has been attributed to problems with the chosen outcome measures. This article reviews the most common outcome measures used, assesses their positive attributes and proposes needs for future research. The Unified Parkinson's disease Rating Scale has been extensively validated and is by far the most common outcome measure used in trials of symptomatic therapy. Ambiguities in the response scale descriptors, poor inter-rater reliability of some items and a lack of items addressing nonmotor features of the disease are being addressed in a revision of the scale. Quality of life outcomes are being used in the minority of clinical trials, and no single generic or disease-specific quality of life measure is being used most frequently. Additional work validating several of the disease-specific instruments is needed. When a generic measure is used, its validity for use in Parkinson's disease must be critically assessed despite its previously established validity in other diseases. With respect to measuring motor complications, significant unmet needs include a consensus as to the best way to define the first motor complication and validating time to the first occurrence of motor complications as a surrogate of future disability and quality of life. Measuring the effectiveness of a potential neuroprotective agent presents unique challenges, particularly since symptomatic effects of the experimental agent or concomitant treatment can obscure any neuroprotective effects. Study designs and biomarkers are being developed that may overcome this problem. Currently, neuroimaging techniques that reflect function of the dopaminergic system are the most promising biomarkers but still require additional validation.
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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 | 0.735 | 0.770 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.012 |
| Bibliometrics | 0.013 | 0.023 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.020 | 0.015 |
| Open science | 0.011 | 0.012 |
| Research integrity | 0.011 | 0.020 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".