P.002 Comprehensive validation of two commercial immunoassays for the biological diagnosis of Alzheimer’s Disease a laboratory diagnostic test
Bibliographic record
Abstract
Background: Plasma pTau217 is a robust biomarker for the diagnosis of Alzheimer’s disease (AD). However, most pTau217 assays are not widely available for clinical testing. We assessed the performance of two commercially available plasma pTau217 immunoassays in a clinical diagnostic laboratory for AD diagnosis. Methods: 219 plasma samples from healthy controls with negative amyloid PET, 115 plasma samples from pathology-confirmed and 263 samples with confirmed amyloid PET were selected. Plasma pTau217 levels were measured using the ALZpath pTau217 assay on the Quanterix HD-X Simoa platform and the Lumipulse pTau217 assay on the Lumipulse G1200 platform at and BC Neuroimmunology Lab and Neurocode USA. Results: For the ALZpath assay, the coefficients were 10.4%, 10.4%, and 9.9%, and for the Fujirebio assay, were 12.1%, 12.2%, and 5.3%, respectively. Sample stability and interference were similar between the two assays, although moderate heterophilic antibody interference and reduced frozen sample stability at -20˚C were observed for the Fujirebio assay. Both assays demonstrated similar clinical performance and differentiated individuals with AD (ALZpath AUC = 0.94; Fujirebio AUC = 0.90). Conclusions: The performance of the two pTau 217 assays was comparable. The clinical separation between the healthy controls and those with Amyloid pathology was nearly complete for both assays.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".