Non-Invasive Screening for Early Cognitive Impairment in Elderly Hyperuricaemic Men Using Transcranial Colour-Coded Duplex Sonography
Bibliographic record
Abstract
Objectives: Hyperuricaemia has been linked to cognitive decline, yet cerebral structural and haemodynamic changes in this population remain poorly defined. We evaluated transcranial colour-coded duplex (TCCD) sonography as a non-invasive screening tool for early mild cognitive impairment (MCI) in elderly hyperuricaemic men. Methods: In this cross-sectional study, 195 men aged ≥ 60 years with hyperuricaemia were stratified by the Montreal Cognitive Assessment (MoCA) into HUA + MCI (MoCA < 26, n = 46) and HUA (MoCA ≥ 26, n = 149) groups. TCCD measured third-ventricle width (TVW) and peak systolic/end-diastolic velocities to calculate resistive (RI) and pulsatility (PI) indices in the middle (MCA) and posterior (PCA) cerebral arteries. Serum uric acid was recorded. Kernel density plots and receiver operating characteristic (ROC) curves assessed diagnostic performance. Results: The HUA + MCI group exhibited higher serum uric acid (508.5 ± 36.3 vs. 492.9 ± 44.0 µmol/L; p = 0.031), greater TVW (0.55 ± 0.11 vs. 0.51 ± 0.08 cm; p = 0.037), and elevated left PCA RI (0.69 ± 0.07 vs. 0.64 ± 0.06) and PI (1.05 ± 0.17 vs. 0.95 ± 0.12; both p < 0.001). ROC analysis identified left PCA PI as the most specific marker (AUC = 0.701; specificity 90.6%; sensitivity 45.7%). Kernel density plots confirmed distinct distributions of key parameters. Conclusions: TCCD-detected ventricular enlargement and raised PCA pulsatility accurately distinguish MCI among hyperuricaemic men. As a non-invasive, accessible technique with high specificity, TCCD may complement MRI and cognitive testing in early screening of at-risk populations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".