Impact of tear optics on the repeatability of Pentacam AXL wave and iTrace in measuring anterior segment parameters and aberrations
Bibliographic record
Abstract
Purpose: To assess impact of tear optics on repeatability of a Scheimpflug device with a Hartmann Shack aberrometer and a ray tracing aberrometer. Methods: One hundred healthy and 100 postrefractive surgery eyes underwent dry eye evaluation including Schirmer's test and tear film break-up time (TBUT). They underwent optical quality analyzer (OQAS, Visio metrics S.L, Terrassa, Spain) to assess objective scatter index (OSI), three scans each on Pentacam AXL wave (OCULUS Optikgerate Gmbh, Wetzlar, Germany), iTrace (Tracey™ Technologies, Texas, USA) for flat, steep keratometry, thinnest corneal thickness, root mean square higher-order aberrations (RMS HOA), RMS lower-order aberrations (LOA), spherical aberrations, RMS COMA. Repeatability of Pentacam AXL wave and iTrace in healthy and postrefractive eyes (OSI >1 vs OSI <1) was studied using within-subject standard deviation (Sw) test-retest repeatability (TRT), coefficient of variation (COV). Results: OSI showed an inverse association with TBUT (P < 0.001). All measurements with Pentacam AXL wave with OSI < 1 had excellent repeatability, intraclass correlation coefficient (ICC) ranging from 0.88 for HOA, to 0.92 for LOA. The Sw, TRT, and COV of all aberration measurements were significantly lower (better) than those of iTrace. In eyes with OSI ≥1, the repeatability with Pentacam AXL wave dropped with ICC ranging from 0.77 for HOA, to 0.84 for LOA with lower Sw, TRT, and COV of all aberration measurements as compared to iTrace. Maximum variation was seen with HOA and minimum with LOA. Conclusion: Tear optics affected repeatability of Pentacam wave and iTrace. Pentacam wave had better repeatability in eyes with a poor tear film as compared to iTrace. Thus, the tear film can impact repeatability of an instrument and it is important to assess the tear film prior to imaging patients, which can change the way we interpret and image these patients.
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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 | 0.003 | 0.014 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".