Plasma amyloid, phosphorylated tau, and neurofilament light for individualized risk prediction in mild cognitive impairment
Bibliographic record
Abstract
Abstract We developed models for individualized risk prediction of cognitive decline in mild cognitive impairment (MCI), using plasma biomarkers of β-amyloid (Aβ), tau, and neurodegeneration. We included MCI patients from the Swedish BioFINDER study (n=148) and the Alzheimer’s Disease Neuroimaging Initiative (ADNI; n=86 for model selection; n=425 for prognostic validation). The primary outcomes were longitudinal cognition and conversion to AD dementia, predicted by plasma Aβ42/Aβ40, P-tau181, and neurofilament light (NfL). A model which included P-tau181 and NfL, but not Aβ42/Aβ40, had the best performance (AUC=0.88 for four-year conversion to AD in BioFINDER, validated in ADNI). The prognostic ability of plasma biomarkers was stronger than a basic model of age, sex, education, and baseline cognition and similar to cerebrospinal fluid biomarkers. Plasma biomarkers, in particular P-tau181 and NfL, may be of high value to identify MCI individuals who will progress to AD dementia in clinical trials and 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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".