Effects of myopia and glaucoma in the prelaminar neural canal and anterior lamina cribrosa using 1060-nm swept-source optical coherence tomography
Bibliographic record
Abstract
Abstract Purpose Investigate the effects of myopia and glaucoma in the prelaminar neural canal and anterior lamina cribrosa using 1060-nm swept-source optical coherence tomography Design Retrospective, cross-sectional study Methods Setting Clinical practice Patient or study population 19 controls (38 eyes); 38 glaucomatous subjects (63 eyes). Inclusion criteria for glaucomatous subjects: i) optic disc neural rim loss; ii) peripapillary nerve fibre layer (NFL) loss on spectral domain optical coherence tomography (SD-OCT); iii) glaucomatous visual field defect with an abnormal pattern standard deviation (P<.05); iv) stable SD-OCT, visual field, and optic disc clinical examination for 6 or more months. Inclusion criteria for control subjects: no evidence of retinal or optic nerve pathology. Exclusion criteria: i) retinal diseases or optic neuropathy other than primary open-angle glaucoma; ii) intraocular pressure less than 10 mmHg or higher than 20 mmHg; iii) ocular media opacities; iv) any surgery-related complication deemed inappropriate for the study. Intervention or observation procedures Swept-source optical coherence tomography Main Outcome Measure(s) Bruchs membrane opening (BMO) and anterior laminar insertion (ALI) dimension, prelaminar neural canal dimension, anterior lamina cribrosa surface (ALCS) depth Results Glaucomatous eyes had more bowed and nasally rotated BMO and ALI, more horizontally skewed prelaminar neural canal, and deeper ALCS than the control eyes. Increased axial length was associated with a wider, longer, and more horizontally skewed neural canal, and decrease in the ALCS depth and curvature. Conclusion Our findings suggest that glaucomatous posterior bowing or cupping of lamina cribrosa can be significantly confounded by the myopic expansion of the neural canal. This may be related to higher glaucoma risk associated with myopia from decreased compliance and increased susceptibility to IOP-related damage of LC being pulled taut.
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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 | 0.002 |
| 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".