EFFECTS OF VASCULAR COMORBIDITY IN PARKINSON'S DISEASE
Bibliographic record
Abstract
Vascular disease and risk factors are common in Parkinson9s disease (PD) and may influence phenotype. Statin therapy may thus be indicated. 1759 recently diagnosed PD cases from a multicentre prospective study underwent a Montreal Cognitive Assessment and the Unified PD Rating Scale part 3 (UPDRS 3). History of vascular events, risk factors and statin usage was recorded. QRISK2 quantified cardiovascular risk. Mean age was 67.5 (SD 9.3), disease duration 1.3 (SD 0.9) years, 65.2% male. 4.7% had prior stroke/TIA, 12.5% cardiac disease, 30.4% hypertension, 27.3% high cholesterol, 20.7% obesity, 7.2% diabetes and 4.6% smokers. Patients with prior stroke/TIA had more cognitive impairment (42.0% versus 25.0%, p<0.001) and postural instability gait difficulty (p=0.02). 32.6% had QRISK2 ≥20%, 30.2% QRISK2 10–20%, and 22.5% QRISK2 <10%. Age (p<0.001), sex (p<0.001), UPDRS 3 (p<0.001) and cognitive impairment (p=0.019) differences were significant across groups; higher QRISK2 patients were therefore older with worse motor and cognitive status. 77.4% of vascular disease cases were prescribed statins, compared to 37.3% for QRISK2 ≥20%. Vascular comorbidity contributes to disease pattern in PD, and statin usage is suboptimal. This has prognostic and treatment implications.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".