Perfusion Area of Macular Choriocapillaris: A Potential Biomarker of Mild Cognitive Impairment in Cerebral Small Vessel Disease Patients
Bibliographic record
Abstract
BACKGROUND: To develop and validate markers for screening mild cognitive impairment (MCI) in cerebral small vessel disease (CSVD) using Swept Source Optical Coherence Tomography Angiography (SS-OCTA). METHOD: Participants with MCI and normal cognition (NC) underwent structural magnetic resonance imaging (S-MRI) and SS-OCTA were prospectively recruited (Dream-10 and FRESH-CSVD study, NCT06164262 and NCT06431711). Patients with Alzheimer's disease (AD) were excluded according to plasma biomarkers test. MCI was defined as a Montreal Cognitive Assessment (MoCA) score ranging from 18 to 26 points, accompanied by a complaint of memory loss. Participants were categorized into development (January 2024 to May 2024) and validation cohorts (June 2024 to September 2024) based on chronological order. LASSO-derived logistic regression analysis was employed to filter potential markers, which was further validated via temporal validation. RESULT: A total of 102 participants were enrolled, with 59.8% (61/102) having MCI. In the development cohort (n = 61), the volume of left transverse temporal gyrus (L-TTG), the volume of right choroid plexus (R-CP) on S-MRI, 0-3mm and 0-6mm oculus Sinister (OS) macular perfusion area (PA) of choriocapillaris (CC) were identified as MCI markers (FDR p < 0.05) (Table 1). In the validation cohort (n = 41), 0-3mm and 0-6mm OS macular CCPA were identified as superior MCI markers (AUC:0.849 and 0.833; sensitivity:0.672 and 0.639; specificity:0.976 and 0.951) with significant better NRI and IDI when compared to other MCI markers (all p < 0.05) (Table 2). CONCLUSION: SS-OCTA, especially OS macular CCPA, holds promise for screening MCI in CSVD patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".