Discrimination of Alzheimer’s disease and non‐Alzheimer’s disease neurocognitive‐disordered patients by assessing CSF biomarkers in Thai population
Bibliographic record
Abstract
Abstract Background CSF biomarkers including β‐amyloid (Aβ) (1‐42), phosphorylated tau (p‐tau) 181 and total tau (t‐tau) are used as ante‐mortem biomarkers to detect Alzheimer’s disease (AD) pathology in cognitively impaired patients. Decreased level of Aβ(1‐42), elevated level of p‐tau 181 and t‐tau are well established in AD. Base on CSF analysis technique and population, cut‐points are varied to distinguish between AD and non‐AD neurocognitive‐disordered patients. This study aimed to evaluate the local cut‐points of CSF biomarkers as diagnostic tools to discriminate AD and non‐AD neurocognitive‐disordered patients in Thai population. Methods We enrolled 74 cognitively impaired patients in memory clinic at Siriraj hospital, Thailand. Participants underwent a standardized diagnostic dementia evaluation, including medical history, physical examination, neuropsychological tests using Thai Mental State Examination (TMSE), Montreal Cognitive Assessment (MoCA) and Clinical Dementia Rating Sum of Boxes score (CDR‐SB), and brain imaging. Patients were clinically diagnosed with each subtype of neurocognitive disorder without knowledge of CSF biomarker results. CSF Aβ(1‐42), p‐tau 181 and t‐tau were measure by ELISA technique. Youden index–based cut‐points of CSF biomarkers were defined using receiver operating characteristic (ROC) to distinguish between AD and non‐AD neurocognitive‐disordered patients. Results 45 (60.8%) patients were diagnosed with AD dementia and 29 (39.2%) patients were diagnosed with non‐AD neurocognitive disorders. Median values of CSF Aβ(1‐42) were significantly lower in AD group than in non‐AD group (371.0 vs. 666.0 pg/mL, p <0.001*). Median values of CSF p‐tau 181 and ratios of CSF p‐tau 181/Aβ(1‐42) were also significantly higher in AD group than in non‐AD group (p‐tau 181; 70.0 vs. 44.0 pg/mL, p = 0.002*) (p‐tau 181/Aβ(1‐42) ratio; 0.180 vs. 0.059, p <0.001*). Whereas t‐tau levels were not statistically different between groups (359.0 vs. 227.0 pg/mL, p = 0.053). Local cut‐points of CSF biomarkers to determine AD dementia were proposed as CSF Aβ(1‐42) ≤538 pg/mL (AUC 0.96, p <0.001*), CSF p‐tau 181 >56 pg/mL (AUC 0.72, p <0.001*) and p‐tau 181/Aβ(1‐42) ratio >0.094 (AUC 0.90, p <0.001*). Conclusion The utility of CSF Aβ(1‐42) and p‐tau 181/Aβ(1‐42) ratio was notably higher in effectively distinguishing Alzheimer's disease from non‐Alzheimer's disease neurocognitive disorder within Thai population.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".