Ophthalmic Artery and Superior Ophthalmic Vein Blood Flow Dynamics in Glaucoma Investigated by Phase Contrast Magnetic Resonance Imaging
Bibliographic record
Abstract
PRECIS: Ophthalmic artery (OA) and superior ophthalmic vein (SOV) blood flow were quantified by phase contrast magnetic resonance imaging (PC MRI) and seemed lower in glaucoma. Venous flow dynamics was different in glaucoma patients with a significantly decreased pulsatility. INTRODUCTION: Studies using color Doppler imaging and optical coherence tomography flowmetry strongly suggested that vascular changes are involved in the pathophysiology of glaucoma, but the venous outflow has been little studied beyond the episcleral veins. This study measured the OA and the SOV flow by PC MRI in glaucoma patients compared with controls. METHODS: Eleven primary open-angle glaucoma patients, with a mean±SD visual field deficit of -2.3±2.7 dB and retinal nerve fiber layer thickness of 92±13 µ, and 10 controls of similar age, were examined by PC MRI. The mean, maximal and minimal flow over cardiac cycle were measured. The variation of flow (ΔQ) was calculated. RESULTS: The OA mean±SD mean flow was 13.21±6.79 in patients and 15.09±7.62 mL/min in controls (P=0.35) and the OA maximal flow was 25.70±12.08 mL/min in patients, and 28.45±10.64 mL/min in controls (P=0.22). In the SOV the mean±SD mean flow was 6.46±5.50 mL/min in patients and 7.21±6.04 mL/min in controls (P=0.81) and the maximal flow was 9.06±6.67 in patients versus 11.96±9.29 mL/min in controls (P=0.47). The ΔQ in the SOV was significantly lower in patients (5.45±2.54 mL/min) than in controls (9.09±5.74 mL/min) (P=0.04). DISCUSSION: Although no significant difference was found, the mean and maximal flow in the OA and SOV seemed lower in glaucoma patients than in controls. The SOV flow waveform might be affected in glaucoma, corroborating the hypothesis of an impairment of venous outflow in those patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".