Optic nerve tortuosity and displacements during horizontal eye movements in healthy and highly myopic subjects
Bibliographic record
Abstract
AIMS: (1) To assess the morphology and 3-dimensional (3D) displacements of the eye globe and optic nerve (ON) in adduction/abduction using MRI. (2) To assess differences between healthy emmetropic and highly myopic (HM) subjects. METHODS: MRI volumes of both eyes from 18 controls and 20 HM subjects in primary gaze, abduction and adduction (15°) were postprocessed. All ONs were manually segmented and fitted to a 3D curve to assess ON tortuosity. ON displacements were evaluated in four quasicoronal planes which were perpendicular to the ON in primary gaze and were 3 mm apart. RESULTS: Axial length was higher in the HM group (28.62±2.60 vs 22.84±0.89 mm; p<0.0001). Adjusted ON tortuosities (ie, ON tortuosities estimated before myopia onset) were lower in HM eyes (0.9063±0.0591) versus controls (1.0152±0.02981) in primary gaze, adduction (0.9023±0.05538 vs 1.0137±0.0299) and abduction (0.9100±0.0594 vs 1.0182±0.0316); p<0.0001 for all cases. In all eyes, ON displacements in adduction were significantly different from those in abduction in the naso-temporal direction (p<0.0001 in all planes) but not in the supero-inferior direction. ON displacements in the posterior segments of the ON were smaller in the HM group in both gaze directions and were larger in the anterior-most ON segment in adduction only. CONCLUSION: The adjusted tortuosity of the ON was significantly lower in HM eyes, suggesting that eyes destined towards HM exhibited higher ON traction forces during eye movements before the onset of myopia. Our ON metrics may be valuable to explore a potential link between eye movements and axial elongation.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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 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".