Predictors of time to requiring dopaminergic treatment in 2 Parkinson's disease cohorts
Bibliographic record
Abstract
The rate of progression of Parkinson's disease (PD) is highly variable. Knowledge of factors associated with disease milestones and commonly used research outcome measures helps with patient counseling and guides the design and interpretation of clinical studies. The objective of the study was to identify prognostic factors for time to acquiring disability requiring dopaminergic therapy that are reproducible within 2 large prospectively followed cohorts. Potential prognostic factors were identified using data from the Deprenyl and Tocopherol Antioxidative Therapy of Parkinsonism (DATATOP) trial, and their reproducibility was examined using data from the Parkinson Research Examination of CEP-1347 trial (PRECEPT). In multivariable analyses of the DATATOP cohort, higher baseline Unified Parkinson's Disease Rating Scale (UPDRS) scores, full-time employment, a lesser smoking history, and onset on the left side were associated with a shorter time to disability requiring dopaminergic therapy. PRECEPT data confirmed the associations of higher baseline UPDRS scores and full-time employment with shorter time to requiring treatment. Any clinical trial using the end point of time to disability requiring dopaminergic therapy should ensure that groups are well balanced with respect to baseline UPDRS scores and the proportion of subjects employed full time and should consider including these variables as covariates in the statistical model for primary analysis of treatment effects. We suspect that individuals employed full time may have a lower threshold for requiring dopaminergic therapy because of occupational demands.
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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 | 0.002 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".