The role of optical coherence tomography in the evaluation of compressive optic neuropathies
Bibliographic record
Abstract
PURPOSE OF REVIEW: Optical coherence tomography (OCT) is a noninvasive imaging tool routinely used in ophthalmology that provides cross-sectional images of the retina. Compression of the anterior visual pathways results in progressive thinning of the retinal nerve fiber layer (RNFL) and macular ganglion cell complex (GCC) and this review will highlight the utility of OCT in evaluating patients with this condition. RECENT FINDINGS: The RNFL and macular GCC have been found to highly correlate with visual function in patients with compressive optic neuropathies. Preoperative RNFL and macular GCC thickness have emerged as the most reliable and consistent prognostic factors for visual recovery after surgery. Patients with an otherwise normal neuroophthalmic examination, including automated perimetry, may have macular GCC or RNFL thinning as the only manifestation of compression, enabling compressive optic neuropathies to be diagnosed at an earlier stage and managed accordingly. SUMMARY: Recent findings indicate that OCT is an important tool in the evaluation of patients with compressive optic neuropathies, particularly for prognosis in patients with visual field defects and diagnosis in patients with preserved or mildly reduced visual function. Anatomical changes detected by OCT may precede visual loss and allow for earlier diagnosis and presumably better visual outcomes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".