Additional file 1 of Antibody-free measurement of cerebrospinal fluid tau phosphorylation across the Alzheimer’s disease continuum
Bibliographic record
Abstract
Additional file 1: Supplementary Table S1. Precursor and product ions used for quantification. Supplementary Table S2. Method reproducibility assessment. Supplementary Table S3. Performance of the pTau biomarkers in the biochemically characterized AD patients and controls. Supplementary Table S4. ROC curve analysis and fold-change to discriminate between amyloid positive and negative groups in TRIAD. Supplementary Table S5. ROC curve analysis and fold-change to discriminate between amyloid positive and negative groups in BioFINDER-2. Supplementary Figure S1. PRM chromatograms of the assayed tau phosphopeptides. Supplementary Figure S2. Scatter plots of phosphorylated peptide abundances in biochemically characterized AD patients and controls. Supplementary Figure S3. Correlations between the pTau PRM assay and Simoa immunoassay. Supplementary Figure S4. ROC curve analysis to discriminate between amyloid positive and negative groups in TRIAD. Supplementary Figure S5. ROC curve analysis to discriminate between amyloid positive and negative groups in BioFINDER-2. Supplementary Figure S6. Fold changes (AD versus CU-) for phospho-epitope concentrations compared with phosphorylation ratios.
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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 | 0.002 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.866 | 0.210 |
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".