AB026. Ocular rigidity is correlated with glaucomatous structural damage
Bibliographic record
Abstract
Background: The rigidity of the corneoscleral shell is an important biomechanical property which could be relevant in the pathophysiology of open-angle glaucoma (OAG). This study aims to evaluate the relationship between ocular rigidity (OR) and glaucomatous damage as represented by structural optical coherence tomography (OCT)-based parameters such as retinal nerve fiber layer (RNFL) and ganglion cell layer+inner plexiform layer (GCL + IPL) thicknesses. These parameters characterize the retinal layers that contain neuronal structures that form the optic nerve. Methods: Sixty-six subjects (37 with early OAG, 11 with moderate to advanced OAG, 16 healthy) were recruited in this study. OR measurements were carried out using a non-invasive clinical method developed by our group. As described in Beaton et al. (2015), this method, which is based on Friedenwald’s equation, involves video-rate OCT imaging and automated choroidal segmentation, as well as dynamic contour tonometry to calculate the OR coefficient. RNFL and macular GCL thicknesses were acquired using the Cirrus SD-OCT (Carl-Zeiss Meditec, Dublin, CA, USA). Correlations between OR and structural parameters in all 66 eyes were assessed using SPSS. Results: Significant correlations were found between OR and the average GCL+IPL thickness (r=0.355, P=0.004) as well as the minimum GCL + IPL thickness (r=0.340, P=0.006). Direct correlations were also found between OR and RNFL thickness in the inferior quadrant (r=0.258, P=0.036) and inferior clock hour (r=0.313, P=0.011). Conclusions: In this study, we found a positive correlation between structural OCT-based parameters and OR, perhaps indicating more structural damage in less rigid eyes. These findings could provide insight unto the pathophysiology of OAG. Further investigation is warranted to confirm the role of OR in glaucoma and elucidate whether there is a subgroup of patients for which OR plays a greater role.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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 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".