Study protocol of the DUtch PARkinson Cohort (DUPARC): a prospective, observational study of de novo Parkinson’s disease patients for the identification and validation of biomarkers for Parkinson’s disease subtypes, progression and pathophysiology
Bibliographic record
Abstract
BACKGROUND: Parkinson's Disease (PD) is a heterogeneous, progressive neurodegenerative disorder which is characterized by a variety of motor and non-motor symptoms. To date, no disease modifying treatment for PD exists. Here, the study protocol of the Dutch Parkinson Cohort (DUPARC) is described. DUPARC is a longitudinal cohort study aimed at deeply phenotyping de novo PD patients who are treatment-naïve at baseline, to discover and validate biomarkers for PD progression, subtypes and pathophysiology. METHODS/DESIGN: DUPARC is a prospective cohort study in which 150 de novo PD subjects will be recruited through a collaborative network of PD treating neurologists in the northern part of the Netherlands (Parkinson Platform Northern Netherlands, PPNN). Participants will receive follow-up assessments after 1 year and 3 years, with the intention of an extended follow-up with 3 year intervals. Subjects are extensively characterized to primarily assess objectives within three major domains of PD: cognition, gastrointestinal function and vision. This includes brain magnetic resonance imaging (MRI); brain cholinergic PET-imaging with fluoroethoxybenzovesamicol (FEOBV-PET); brain dopaminergic PET-imaging with fluorodopa (FDOPA-PET); detailed neuropsychological assessments, covering all cognitive domains; gut microbiome composition; intestinal wall permeability; optical coherence tomography (OCT); genotyping; motor and non-motor symptoms; overall clinical status and lifestyle factors, including a dietary assessment; storage of blood and feces for additional analyses of inflammation and metabolic parameters. Since the start of the inclusion, at the end of 2017, over 100 PD subjects with a confirmed dopaminergic deficit on FDOPA-PET have been included. DISCUSSION: DUPARC is the first study to combine data within, but not limited to, the non-motor domains of cognition, gastrointestinal function and vision in PD subjects over time. As a de novo PD cohort, with treatment naïve subjects at baseline, DUPARC provides a unique opportunity for biomarker discovery and validation without the possible confounding influences of dopaminergic medication. TRIAL REGISTRATION: NCT04180865; registered retrospectively, November 28th 2019.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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; 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".