Head-to-head comparison of plasma p-tau217 immunoassays for incipient Alzheimer’s disease in community cohorts
Bibliographic record
Abstract
ABSTRACT Background Plasma p-tau217 is a promising biomarker for detecting incipient AD pathology, but direct comparison of different p-tau217 assays in community-based cohorts are limited. Methods We evaluated two cohorts from southwestern Pennsylvania, USA; the MYHAT-NI sub-study, which included two-year longitudinal follow-up neuroimaging assessments of Aβ, tau, and cortical thickness; and the Human Connectome Project/CoBRA, targeting a 50:50 split of self-identified Black and non-Hispanic White individuals. Plasma p-tau217 was measured using four different assays: Lumipulse, Johnson&Johnson, ALZpath, and NULISA. Aβ and tau pathologies were assessed with [ 11 C]PiB PET and [ 18 F]Flortaucipir PET, respectively. Clinical Dementia Rating (CDR) and Montreal Cognitive Assessment were used to assess cognitive performance. Results We included 344 participants (MYHAT-NI: n=111, median age 76 [IQR: 72-80], 54% female; HCP/CoBRA: n=234, median age 62 [IQR: 52-70], 65% female). All four p-tau217 assays exhibited moderate to strong cross-platform correlations (Spearman correlations of 0.40 – 0.86), and statistically equivalent AUCs (of 0.84-0.90) for determining Aβ positivity. Conclusions Our findings showed strong equivalent performances of plasma p-tau217 assays to identify amyloid positivity across two highly diverse cohorts of community-dwelling older adults.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| 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".