Estimating Tear Film Spread and Stability Through Tear Hydrodynamics
Bibliographic record
Abstract
PURPOSE: The stability and ease of spread of the tear-film over the contact lens surface may be an indicator of contact lens surface dewetting. The present in vivo methods of determining lens dewetting are complex. This study introduces a novel and objective way of determining the upward spread and stability of the tear-film through measurement of tear-film particle dynamics. METHODS: Ten adapted contact lens wearers wore the same type of contact lens. Using a video camera mounted to a slit-lamp, the tear-film spread over the lens surface was recorded after a blink, at 8× magnification and capture rate of 30 frames per second, at morning after lens insertion, and after 8 h of lens wear. Images from 20 videos, without blinks and without an observable change in fixation were analyzed without any further postprocessing of the images. Using a customized calibrated ImageJ macro for particle tracking, the velocity of naturally occurring reflective particles was determined. The results were analyzed using the R program and ProFit. RESULTS: The results established that the upward particle velocity was highest immediately after a blink and declined with time. The spread of the tear film measured through upward particle velocity was different on lens insertion than after 8 h of lens wear (p = 0.001). The exponential time constants ± SE were 346.02 ± 29.0 for lens insertion at morning and 1413.13 ± 419.6 after 8 h of lens wear. CONCLUSIONS: A novel and non-invasive way to measure in vivo spread and stability of the prelens tear-film has been developed. Additional studies are needed to understand whether this simple measure is able to differentiate the performance of different soft contact lenses and how this method may help in the understanding other aspects of lens performance such as non-invasive tear breakup time, surface deposition, and lens comfort.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".