Risk Score Predicting Primary Open-Angle Glaucoma Patients With Vascular Predisposition
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Bibliographic record
Abstract
Purpose: We tested the hypothesis that a questionnaire-based risk score predicts the prevalence of patients with primary open-angle glaucoma (POAG) with vascular predisposition. Methods: The Flammer Syndrome Questionnaire (FSQ) was used to determine vascular risk scores in 823 healthy subjects and 512 patients with POAG. Next, we characterized blood flow pulsatility changes within the optic nerve head (ONH) in Flammer syndrome (FS) using laser speckle flowgraphy (LSFG) in 358 eyes of 206 patients with normal-tension glaucoma (NTG). Last, we examined the association between changes in Mean blur rate (MBRAve), an LSFG-derived ONH blood flow measurement, during cold provocation and the FSQ risk score in 56 eyes of 56 patients with NTG. Results: Five FSQ-related symptoms were significantly associated in patients with POAG patients; cold hands/feet (odds ratio [OR] = 1.82), low blood pressure (BP; OR = 3.29), increased response to drugs (OR = 2.27), underweight (OR = 1.99), and tendency toward perfectionism (OR = 1.88). The vascular risk score showed the best discriminative accuracy in differentiating healthy subjects from patients with NTG (area under the curve [AUC] = 0.73). In the NTG eyes, ONH pulsatile blood flow in the FS group was characterized by greater pulsatility. Moreover, the negative correlation between the high FSQ risk score and the cold-induced ONH blood flow reduction was pronounced in eyes with NTG (correlation coefficient = -0.41). Conclusions: The FSQ risk score can be a screening tool to identify patients with POAG with increased vascular stiffness and further reduced ONH blood flow during cold stress. Translational Relevance: The vascular risk score may help tailor individual glaucoma care.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it