Ocular surface parameters repeatability and agreement —A comparison between Keratograph 5M and IDRA
Bibliographic record
Abstract
PURPOSE: To evaluate the repeatability and agreement in dry eye measurements using Oculus Keratograph 5M (K5M) and SBM Sistemi IDRA (IDRA). METHODS: A total of 108 participants were enrolled and 108 eyes were evaluated. Tear meniscus height (TMH) and first and average non-invasive break-up time (NIBUT) were measured using the K5M and IDRA (order randomly assigned). TMH was measured using the built-in caliper tool while NIBUT was computed by the automatic algorithm of the instruments. RESULTS: The Bland Altman plots analysis showed a good agreement between the two instruments for TMH (95 % Limits of Agreement (LoA), -0.17 to 0.16), but not the first NIBUT (95 % LoA, -8.13 to 14.79) and average NIBUT (95 % LoA, -7.89 to 10.32). The values of the first and average NIBUT measured using IDRA were significantly shorter than in K5M (difference = median (IQR) -2.75 (-6.48- -0.28)s, p < 0.001 and difference = median (IQR) -1.65 (-3.97-1.89)s, p = 0.008 respectively). The TMH (p = 0.037) and NIBUT average (p = 0.033) measured by K5M, as well as the TMH (p = 0.040) measured by IDRA, exhibited unstable measurements across the three measurement times. The remaining parameters exhibited stability with three repeated measurements. CONCLUSION: The NIBUT measurements are not interchangeable between IDRA and K5M, while the TMH was little difference between the two instruments. It is important to exercise caution when using different ocular surface analyzers to minimize errors in comparing multiple 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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".