Clinical applications of optical coherence tomography (OCT) in glaucoma
Bibliographic record
Abstract
Visual field (VF) testing has been the mainstay for diagnosing and monitoring glaucoma. However, relying solely on VF can delay the patient’s diagnosis in the early stages of the disease, as the structural changes are known to precede the functional changes and VF defects may not be clinically detectable until at least 25-35% of retinal ganglion cells (RGCs) are lost. This concept highlights the importance of alternative diagnostic modalities such as optical coherence tomography (OCT). OCT’s ability to reliably segregate and quantify the thickness of retinal layers has allowed earlier detection of glaucoma, up to 6 years before the onset of any detectable VF loss. Compared to VF, OCT is less time-consuming and is less dependent on the patient’s cooperation and test-taking ability. There are a few commercially available spectral domain OCT (SD-OCT) machines that are routinely used in glaucoma clinics. These devices are fundamentally similar with comparable performance, but their scanning protocols and segmentation algorithms are not analogous; thus, the measured parameters may not necessarily be interchangeable between devices and the values should be interpreted relative to the normative databases specific to each machine. In this review, we present the clinical applications of OCT imaging in glaucoma and share some clinical pearls and pitfalls.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".