The impact of preanalytical variables on measuring cerebrospinal fluid biomarkers for Alzheimer's disease diagnosis: A review
Bibliographic record
Abstract
INTRODUCTION: Cerebrospinal fluid (CSF) biomarkers have the potential to improve the diagnostic accuracy of Alzheimer's disease, yet there is a lack of harmonized preanalytical CSF handling protocols. METHODS: This systematic review summarizes the current literature on the influence of preanalytical variables on CSF biomarker concentration. We evaluated the evidence for three core CSF biomarkers: β-amyloid 42, total tau, and phosphorylated tau. RESULTS: The clinically important variables with the largest amount of conflicting data included the temperature at which samples are stored, the time nonfrozen samples can be stored, and possible effects of additives such as detergents, blood contamination, and centrifugation. Conversely, we discovered that there is consensus that tube material has a significant effect. DISCUSSION: A unified CSF handling protocol is recommended to reduce preanalytical variability and facilitate comparison of CSF biomarkers across studies and laboratories. In future, experiments should use a gold standard with fresh CSF collected in low binding tubes.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".