Cerebrospinal Fluid Biomarkers Mirror Rate of Cognitive Decline
Bibliographic record
Abstract
The ability to predict future decline in cognitive systems using the cerebrospinal fluid (CSF) biomarkers 42 amino acid form of amyloid-β (Aβ42) and total tau (T-tau) is not fully understood. In a clinical sample ranging from cognitively healthy to dementia (n = 326), linear regression models were performed in order to investigate the ability of CSF biomarkers to predict cognitive decline in all cognitive domains from baseline to 2-year follow-up. Gender, age, and years of education were included as covariates. In patients with subjective cognitive impairment, T-tau had a small impact on executive functions (r2 = 0.07). T-tau had a small to moderate influence (r2 = 0.06-0.11) on all cognitive functions with the exception of visuospatial functions in patients with mild cognitive impairment (MCI). In patients with dementia, the impact of T-tau was large (r2 = 0.29) on semantic memory. Aβ42 had a small effect (r2 = 0.07) on speed and executive functions in MCI. In patients with dementia, Aβ42 had a moderate influence (r2 = 0.13-0.24) on semantic and verbal working memory/fluency. Our results speak in favor of the notion that CSF biomarkers reflect the rate of cognitive decline across the continuum of cognitive impairment from healthy to dementia. CSF predicted subsequent decline in more cognitive domains among MCI cases, but the impact was most pronounced in patients with dementia.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".