The Association between the Pulsatile Choroidal Volume Change and Ocular Rigidity
Bibliographic record
Abstract
Purpose: To assess the relationship between the pulsatile choroidal volume change (DV) and ocular rigidity (OR), an important biomechanical property of the eye.Design: This is a prospective cross-sectional study.Subjects: Two hundred seventeen participants (235 eyes) were included in this study.Of those, 18 eyes (18 participants) had exudative retinal disease, and 217 eyes (199 participants) had open-angle glaucoma (39.2%), suspect discs (12.4%), ocular hypertension (14.3%), or healthy eyes (34.1%).Methods: Pulsatile choroidal volume change was measured using dynamic OCT, which detects the change in choroidal thickness during the cardiac cycle.Ocular rigidity was measured using an invasive procedure as well as using a validated optical method.Correlations between DV and OR were assessed in subjects with healthy eyes, eyes with glaucoma, or eyes with exudative retinal disease.Main Outcome Measures: Ocular rigidity and pulsatile ocular volume change.Results: In 18 eyes where OR was obtained invasively and DV was obtained noninvasively, a significant correlation was found between DV and OR (r s ¼ À0.664, P ¼ 0.003).Similarly, a strong inverse correlation was found between the noninvasive measurements of both DV and OR (r s ¼ À0.748, P < 0.001) in a large cohort and maintained its significance across diagnostic groups (a more compliant eye is associated with greater DV).No correlation was found between DV and age, blood pressure, intraocular pressure, axial length, or diagnosis (P !0.05).Mean DV was 7.3 Æ 3.4 mL for all groups combined with a range of 3.0 to 20.8 mL.Conclusions: These results suggest an association between the biomechanics of the corneoscleral shell and pulsatile ocular blood flow, which may indicate that a more rigid eye exerts more resistance to pulsatile choroidal expansion.This highlights the dynamic nature of both blood flow and biomechanics in the eye, as well as how they may interact, leading to a greater understanding of the pathophysiology of ocular disease.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".