sTREM2 in discordant CSF Aβ<sub>42</sub> and p‐tau181
Bibliographic record
Abstract
Abstract INTRODUCTION Little is known about the factors underpinning discordant cerebrospinal fluid (CSF) amyloid beta (Aβ)42 versus p‐tau181/Aβ42 or CSF Aβ42 versus Aβ positron emission tomography (PET). METHODS We stratified 570 non‐demented Alzheimer's Disease Neuroimaging Initiative (ADNI) participants by Aβ PET and further by CSF Aβ42 or p‐tau181/Aβ42. We used analysis of covariance testing adjusting for covariates, followed by Tukey post hoc pairwise comparisons, to compare CSF soluble triggering receptor expressed on myeloid cells‐2 (sTREM2) across four participant groups: CSF+ Aβ42 with CSF− p‐tau/Aβ42, CSF− Aβ42 with CSF+ p‐tau/Aβ42, and concordant CSFAβ42/CSFp‐tau/Aβ42. We also compared sTREM2 across discordant and concordant CSFAβ42/PET. RESULTS Regardless of Aβ PET status, CSF+Aβ42 with CSF−p‐tau/Aβ42 had lower sTREM2 than CSF−Aβ42 with CSF+p‐tau/Aβ42. CSF sTREM2 was similarly also associated with discordant CSF Aβ42 /PET. DISCUSSION Our study suggests the potential roles of sTREM2 in discordant CSF Aβ42 and p‐tau181/Aβ42 and discordant CSFAβ42/PET. Low‐ and high‐CSF sTREM2 may affect the accuracy of p‐tau181/Aβ42 during the clinical work‐up of AD. Highlights 17% of non‐demented older adults had discordant CSF Aβ42 versus p‐tau181/Aβ42. sTREM2 differed between discordant cases of CSF Aβ42 versus p‐tau181/Aβ42. 20% of non‐demented older adults had discordant CSF Aβ42 versus Aβ PET. sTREM2 also differed between discordant cases of CSF Aβ42 versus Aβ PET. p‐tau181/Aβ42 may miss 6.7% of PET+ non‐demented older adults with low sTREM2.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".