Patterns of Outcome Measurement in Parkinson’s Disease Clinical Trials
Bibliographic record
Abstract
The study examines the pattern of use and clinimetric properties of clinical endpoints used in randomized trials for Parkinson's disease (PD). Randomized drug trials for PD were identified through a Medline search conducted from January 1966 until August 1998. The endpoints used in these trials were abstracted. Reports examining the clinimetric properties of the disease-specific scales used in these trials were also abstracted. Data regarding the consistency, accuracy, discrimination and feasibility of scales used in at least 10% of trials were determined. One hundred and thirty-seven articles met the inclusion criteria; 70.8% of trials used some clinical scale for PD as an endpoint. The Unified Parkinson's Disease Rating Scale (UPDRS) was the most commonly used scale (32.8%). Factors independently associated with the use of the UPDRS included: the study location in the US, mean age of subjects over 62.7 years and publication after 1994. The UPDRS was more thoroughly studied and superior in most clinimetric domains compared to scales developed earlier. Few studies included generic health status (2.9%) or cognitive measures (16.8%) as secondary endpoints. There have been definite improvements in the area of disease-specific measurement in PD trials since the introduction of the UPDRS. The results of studies that used instruments with poor or unreported clinimetric properties should be critically interpreted.
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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 | 0.635 | 0.889 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.026 | 0.038 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".