Primary Open Angle Glaucoma is Associated with MR Biomarkers of Cerebral Small Vessel Disease
Bibliographic record
Abstract
This prospective study tests the hypotheses that: 1) glaucoma is associated with evidence of cerebral small vessel disease; 2) that imaging biomarkers of cerebral small vessel disease in POAG and NTG will show different characteristics. 12 normal controls, 7 patients with primary open angle glaucoma (POAG) and 9 patients with normal tension glaucoma (NTG) were recruited. Ophthalmological clinical assessment and MR imaging of the brain were performed. MR imaging was used to quantify white matter lesion load, frequency of dilated perivascular spaces (PVS) and abnormalities in cerebral hydrodynamics. Patients with POAG had significantly greater white matter lesion load (p < 0.05), more PVS in the centrum semiovale (p < 0.05) and had higher overall PVS scores than controls (p < 0.05). In the POAG group, optic cup-to-disc ratio (CDR) was positively correlated with deep white matter hyperintensities (R(2) = 0.928, p < 0.01). Mean deviation on the Humphrey visual field assessment was negatively correlated with deep white matter lesion load (R(2) = -0.840, p < 0.01), total white matter lesion load (R(2) = -0.928, p < 0.01) and total PVS (R(2) = -0.820, p < 0.01). MR evidence of cerebral small vessel disease is strongly associated with a diagnosis of POAG and with the severity of abnormalities in CDR and visual field.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".