Characterization of pre‐analytical sample handling effects on a panel of Alzheimer's disease–related blood‐based biomarkers: Results from the Standardization of Alzheimer's Blood Biomarkers (SABB) working group
Bibliographic record
Abstract
Abstract Introduction Pre‐analytical sample handling might affect the results of Alzheimer's disease blood‐based biomarkers. We empirically tested variations of common blood collection and handling procedures. Methods We created sample sets that address the effect of blood collection tube type, and of ethylene diamine tetraacetic acid plasma delayed centrifugation, centrifugation temperature, aliquot volume, delayed storage, and freeze–thawing. We measured amyloid beta (Aβ)42 and 40 peptides with six assays, and Aβ oligomerization‐tendency (OAβ), amyloid precursor protein (APP) 699‐711 , glial fibrillary acidic protein (GFAP), neurofilament light (NfL), total tau (t‐tau), and phosphorylated tau181. Results Collection tube type resulted in different values of all assessed markers. Delayed plasma centrifugation and storage affected Aβ and t‐tau; t‐tau was additionally affected by centrifugation temperature. The other markers were resistant to handling variations. Discussion We constructed a standardized operating procedure for plasma handling, to facilitate introduction of blood‐based biomarkers into the research and clinical settings.
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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 | 0.029 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| 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 | 0.000 |
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".