Comparison of Optical Low-Coherence Reflectometry and Ultrasound Pachymetry in Measuring Corneal Graft Thickness
Bibliographic record
Abstract
PURPOSE: To compare optical low-coherence reflectometry (OLCR) and ultrasound pachymetry in measuring corneal graft thickness in patients after keratoplasty. METHODS: We retrospectively measured the central graft thickness in 41 eyes of 41 patients with the OLCR pachymeter (Haag Streit, Koeniz, Switzerland) and the SP-2000 contact ultrasound pachymeter (Tomey, Nagoya, Japan). Five separate measurements were performed on each eye with both methods. Mean, SD, repeatability, and coefficient of variation of measurements were calculated, and the correlation between the 2 methods was studied with Spearman regression. RESULTS: Mean central graft thickness was 546 +/- 51 (SD) microm with the contact ultrasound pachymeter and 546 +/- 47 microm with the OLCR pachymeter. The correlation between both methods was strong (rs = 0.96). No significant differences in mean SD of measurements were observed between OLCR pachymetry (mean SD = 4.66 microm) and contact ultrasound pachymetry (mean SD = 4.88 microm). The repeatability of both methods was comparable (P = 0.06) and high (the average coefficient of variation of the central corneal graft thickness was 0.9% with both pachymeters). The postoperative time did not affect the correlation between both pachymeters (P > 0.05). CONCLUSIONS: Central corneal graft thickness values obtained with the OLCR pachymeter were similar to those obtained with a contact ultrasound pachymeter. In some cases of lamellar keratoplasty, the corneal refractive index could change at the interface level that could affect OLCR measurements.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".