Clinical validation of new alzheimer disease diagnosis tools based on plasma p-Tau217
Bibliographic record
Abstract
Nowadays, there is an unmet need for reliable and minimally-invasive diagnosis tools capable of detecting Alzheimer's disease at early stages. Such tools could significantly reduce the reliance on confirmatory tests that are invasive and costly, such as cerebrospinal fluid (CSF) biomarkers and neuroimaging. The aim of this study is to validate previously developed diagnosis tools (multivariate models and plasma p-Tau217 levels) in three independents cohorts. For this, a cohort was obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) including some variables (age, Apolipoprotein E (ApoE) genotype, plasma p-Tau217, CSF biomarkers) (n = 113); and two cohorts from cognitive disorders units (Hospital Universitari i Politècnic La Fe (HUiPLaFe, n = 163), Hospital Doctor Peset (n = 31)), whose plasma samples were analysed to determine plasma p-Tau217, and to evaluate the previous diagnosis tools performance. For the cohort from HUiPLaFe, the multivariate model (plasma p-Tau217, age, ApoE genotype) showed a sensitivity of 94.9% and a specificity of 88.2%; for the cohort from Hospital Doctor Peset, the sensitivity was 100% and specificity 80%; for the ADNI cohort, sensitivity was 89.5% and specificity 39.5%. Regarding the plasma p-Tau217 levels, the results were satisfactory for the cognitive disorders units; while ADNI cohort showed very low specificity. In conclusion, the multivariate model was clinically validated in independent cohorts from clinical units, representing its first step for implementation.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".