Biomarker Assessment in Parkinson’s Disease Dementia and Dementia with Lewy Bodies by the Immunomagnetic Reduction Assay and Clinical Measures
Bibliographic record
Abstract
Background: Plasma biomarker assays provide an opportunity to reassess whether Alzheimer's disease, Parkinson's disease dementia (PDD), and dementia with Lewy bodies (DLB) plasma biomarkers are diagnostically useful. Objective: We hypothesized that immunomagnetic reduction (IMR) of plasma biomarkers could differentiate between patients with PDD and DLB and healthy patients when combined with established clinical testing measures. Methods: ), total tau (t-tau), phosphorylated tau at threonine 181 (p-tau181), and α-synuclein (α-syn). Receiver operating characteristic curve (ROC) analysis was used to obtain sensitivity, specificity, and area under the ROC curve. Biomarker results were combined with clinical measures from the Unified Parkinson's Disease Rating Scale (UPDRS), Montreal Cognitive Assessment, and Hoehn-Yahr stage to optimize diagnostic test performance. Results: ×α-syn. Patients with DLB had higher p-tau181 than those with PDD and healthy participants and were distinguishable by their concentrations of α-syn×p-tau181. Plasma α-syn plus UPDRS versus either test alone increased sensitivity, specificity, and AUC when healthy patients were compared with those with PDD and DLB. Combined clinical examination scores and plasma biomarker products demonstrated utility in differentiating PDD from DLB when p-tau181 was combined with UPDRS, α-syn was combined with UPDRS, and α-syn×p-tau181 was combined with UPDRS. Conclusions: In this pilot study, IMR plasma p-tau181 and α-syn may discriminate between PDD and DLB when used in conjunction with clinical testing.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".