The two cut‐offs approach for plasma p‐tau217 in detecting Alzheimer's disease in subjective cognitive decline and mild cognitive impairment
Bibliographic record
Abstract
BACKGROUND: The study aimed to explore the applicability of plasma phosphorylated tau (p-tau)217 in identifying patients with subjective cognitive decline (SCD) and mild cognitive impairment (MCI) carrying Alzheimer's disease (AD) pathology in real-world settings. METHODS: Fifty SCD, 87 MCI, and 50 AD-demented patients underwent blood collection to dose plasma p-tau217 with a fully automated Lumipulse G600II assay. Patients were classified according to the Revised Criteria of the Alzheimer's Association Workgroup as Core1+ or Core1- (based on amyloid positron emission tomography, cerebrospinal fluid [CSF] amyloid beta [Aβ]42/Aβ40, CSF p-tau181/Aβ42). RESULTS: Plasma p-tau217 was accurate for discriminating between Core1+ and Core1- patients (area under the curve = 0.92) with an optimal cut-off value of 0.274 pg/mL, revealing good accuracy (86.29%), positive predictive value (PPV; 88.18%), and negative predictive value (NPV; 83.09%). The two cut-offs approach (0.229-0.516 pg/mL) showed higher accuracy (91.11%), a PPV of 96.25% and a NPV of 83.63%. CONCLUSION: The two cut-offs approach provides for stronger accuracy, PPV, and NPV than a single cut-off, making reliable the clinical application of plasma p-tau217 for early detection of AD in real-world settings. Highlights: Plasma phosphorylated tau (p-tau)217 was highly accurate in detecting Alzheimer's disease (AD) pathology.The two cut-offs approach increased plasma p-tau217 accuracy for AD diagnosis.Even when measured with immunoassay, p-tau217 is a good biomarker for AD diagnosis.Transition of p-tau217 from research setting to clinical practice seems feasible.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".