Analytical considerations and clinical utility of plasma phosphorylated Tau217
Bibliographic record
Abstract
Blood-based biomarkers are an easily available and practical tool for Alzheimer's disease (AD) screening and diagnosis. Plasma phosphorylated Tau217 (p-tau217) is the front-runner candidate for AD diagnosis due to its strong correlation with core AD pathology determined either by cerebrospinal fluid biomarker (CSF) and positron emission tomography (PET) or postmortem examination. While plasma p-tau217 is firmly associated with AD pathology, it is crucial to evaluate its performance in distinguishing AD from mixed pathologies, as brain autopsies have shown the coexisting of AD pathology with other related types of dementia. Moreover, the measurement of AD biomarkers will be a crucial element in defining eligibility for disease-modifying treatment in clinical practice. Moreover, plasma p-tau217 is a highly efficacious biomarker in the early detection of Aβ pathology, making it a feasible test for AD screening in clinical practice. Several assays, including the ALZpath p-tau217 assay and the Fujirebio plasma p-tau217 assay, have been made commercially available for research use. A few studies analytically and clinically have validated these immunoassays as laboratory diagnostic tests for AD diagnosis and differentiating from non-AD neurodegenerative disorders in clinical practice.
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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 | 0.018 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".