A longitudinal program for biomarker development in Parkinson's disease: A feasibility study
Bibliographic record
Abstract
Long-term follow up is necessary to understand the natural history of treated Parkinson's disease (PD). The Longitudinal and Biomarker Study in PD (LABS-PD) is an observational study designed to prospectively measure the evolution of motor and non-motor features of PD and sample promising biomarkers from early to late stage illness. LABS-PD is organized on the premise that cohorts from completed clinical trials can be re-recruited for long-term follow up. LABS-PD will eventually contain multiple cohorts, but to test the feasibility of the strategy, we examined enrollment and biomarker sampling in the initial cohorts. The first PD cohort (PostCEPT) comes from the de novo clinical trial of a mixed lineage kinase inhibitor (PRECEPT). We assessed the recruitment from PRECEPT to PostCEPT, the ability to link data from the two studies, and sample collection for a variety of biomarkers. A total of 537 of 709 eligible PRECEPT subjects (76%) enrolled in PostCEPT; 509 (95%) had repeat dopamine transporter imaging. PRECEPT clinical and imaging data were successfully linked to PostCEPT, to provide 3 to 4 year follow-up. A biomarker sub-study enrolled over 100 PD cases from PostCEPT and 100 controls to measure olfaction and blood markers of gene expression, alpha-synuclein, and proteomic profiles. We were also successful in linking clinical and biomarker data to DNA samples that have been collected in the publicly accessible Coriell repository. The PostCEPT cohort and associated studies strongly support the feasibility of the LABS-PD approach of retaining and repurposing clinical trial cohorts to collect longitudinal clinical and biomarker data.
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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 | 0.027 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".