Repeatability of Pachymetry and Thinnest Point Localization Using a Fourier‐Domain Optical Coherence Tomographer
Bibliographic record
Abstract
PURPOSE: To evaluate the repeatability of pachymetry measures at six distinct locations using a Fourier-domain optical coherence tomographer. The location of the thinnest point was also described using vector analysis, and repeatability of the localization of the thinnest point was calculated. METHODS: The right eye of 25 healthy subjects had 3 images of their right cornea captured using the pachymetry protocol and anterior segment lens of a commercially available Fourier-domain optical coherence tomographer (RTVue-100, Optovue, Fremont, CA). The first two images were used to quantify repeatability in the central (2 mm) and paracentral (2-5 mm) cornea. Cartesian coordinates representing the location of the thinnest point were used to determine the mean location in relation to the central cornea. RESULTS: Mean central thickness was 536.8 ± 31.1 μm, and the mean minimum thickness was 526.4 ± 33.1 μm. The thickest paracentral zone was the superior cornea and the thinnest was the temporal cornea. Intraclass correlation values ranged between 0.969 and 0.996, and the mean coefficient of repeatability was 1.74% across all locations evaluated. The thinnest point was located in the inferior temporal region in 80% of eyes and was an average distance of 1.01 mm from the corneal apex, subtending an angle of 26.7° from the horizontal. CONCLUSIONS: The RTVue-100 provides a highly repeatable measure of corneal thickness at various locations. The instrument is capable of describing and consistently duplicating the location of the thinnest point of the cornea; however, further comparative work with other clinical imaging modalities is required.
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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 | 0.006 |
| 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".