Effect of mire spacing on the subjective non-invasive break-up time measurement: a comparison of instruments
Bibliographic record
Abstract
CLINICAL RELEVANCE: Non-invasive break-up time (NIBUT) has become the measurement of choice to evaluate stability of the tear film. Tear film stability is essential in the diagnosis and follow-up of dry eye disease. BACKGROUND: Instruments used to measure NIBUT have different spacing of luminous mires, but the influence of this characteristic on the measurement of NIBUT is poorly understood. This study aimed at comparing the characteristics of five different instruments used to measure NIBUT and to determine if the mire spacing had an impact on the measured NIBUT. METHODS: Mire spacing of the instruments was assessed using a calibration sphere. Two raters then measured the NIBUT of 20 participants (20 eyes) with normal ocular surfaces three consecutive times with five different instruments (Oculus Placido Disk, Keeler Tearscope Plus with the Coarse and the Fine Grid, Medmont E300 Topographer and Oculus Keratograph 5M). A two-way repeated measure analysis of variance was performed to compare the NIBUT data and correlation values (intra-class and Spearman) were computed for intra-rater agreement and test-retest reliability. RESULTS: The Tearscope Plus with the Fine Grid was the instrument that presented the finest mire spacing. Two instruments, the Medmont E300 and the Keratograph gave shorter NIBUTs compared to the Tearscope Plus with the Fine Grid. All instruments had good inter-rater agreement. The Medmont E300 had a less good test-retest reliability for one of the raters. Bland-Altman plots were used to compare all the instruments with the one having the finest mire spacing. A positive proportional bias between the Tearscope Plus using the Fine Grid and the Coarse Grid was found. CONCLUSION: The topography-based instruments had shorter subjective NIBUT values in non-dry eye participants compared to the other instruments. Mire spacing is not the most definitive factor that impacts the NIBUT value.
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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 | 0.013 | 0.062 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".