Mapping Pulsatile Optic Nerve Head Deformation Using OCT
Why this work is in the frame
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Bibliographic record
Abstract
Objective: To develop a noninvasive technique to quantitatively assess the pulsatile deformation due to cardiac contractions of the optic nerve head (ONH). Design: Evaluation of a diagnostic test or technology. Participants: Healthy subjects with no history of refractive surgery, divided into 2 cohorts on the basis of their axial length (AL). Methods: We present a noninvasive technique to quantitatively assess the pulsatile deformation of the ONH tissue by combining high-frequency OCT imaging and widely available image processing algorithms. We performed a thorough validation of the approach, numerically and experimentally, evaluating the sensitivity of the method to artificially induced deformation and its robustness to different noise levels. We performed deformation measurements in cohorts of healthy (n = 9) and myopic (n = 5) subjects in different physiological strain conditions by calculating the amplitude of tissue displacement in both the primary position and abduction. The head rotation was measured using a goniometer. During imaging in abduction, the head was rotated 40° ± 3°, and subjects were instructed to direct their gaze toward the OCT visual target. Main Outcome Measures: Pulsatile tissue displacement maps. Results: < 0.005). Conclusions: The computational pipeline demonstrated good reproducibility and had the capacity to accurately map the pulsatile deformation of the optic nerve. In a clinical setting, we detected physiological changes in normal subjects supporting its translation potential as a novel biomarker for the diagnosis and progression of optic nerve diseases.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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