A Blood Screening Test for Dementia with Lewy Bodies for Primary Care.
Bibliographic record
Abstract
Introduction: We sought to cross-validate our previously published blood test for detecting Dementia with Lewy Bodies (DLB). Method: Plasma samples were analyzed on 428 individuals (DLB n=89, Parkinson's Disease (PD) without dementia n=126, Alzheimer's Disease (AD) n=108, Normal Controls (NC) n=105). Results: The proteomic profile discriminated DLB and PD from NC with an AUC (Area Under the Curve) of 0.96 with demographics of age, sex and education. The proteomic profile also distinguished DLB from the PD group with an AUC of 0.92 with demographics. It further distinguished DLB, PD and AD from NC with an AUC of 0.95 as well as DLB from AD with an AUC of 0.92 with demographics. Discussion: This data provides additional evidence of the potential utility of a multi-tiered blood-based proteomic screening method for detecting DLB and distinguishing DLB from NC, PD and AD that can be implemented in primary care settings to aid in the referral process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".