Tear Breakup Dynamics: A Technique for Quantifying Tear Film Instability
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to develop a novel, quantitative measurement of tear film breakup dynamics (TBUD) to study the phenomenon of tear breakup in dry eye and control subjects and its impact on dry eye symptoms. METHODS: Ten control and 10 dry eye subjects completed the Dry Eye (DEQ) and other questionnaires. After the instillation of sodium fluorescein, subjects kept the tested eye open for as long as possible, similar to a staring contest, while tear film breakup was videotaped (S-TBUD). The maximum blink interval (MBI) and tear breakup time (TBUT) were measured from digital movies by a masked observer. Individual frames of movies were converted to gray-scale images, maps of relative tear film fluorescence were generated, and the total area of tear breakup (AB) of the exposed cornea was quantified. RESULTS: On average, dry eye subjects demonstrated a higher AB and shorter TBUT and MBI, but only the AB was significantly different (p = 0.023). Subjects most often used the descriptors stinging and burning to describe their sensations during staring trials. The AB showed a high correlation between eyes and with some DEQ symptom measures. CONCLUSIONS: These methods allow objective quantification and tracking of the phenomenon of tear breakup. Our results suggest that tear breakup stresses the corneal surface, resulting in stimulation of underlying nociceptors. The tear film of dry eye subjects was less stable than controls. They had a larger AB measured from the last video frame before MBI (i.e., just before blinking) than did controls. This perhaps reflects adaptation to the repeated stress of tear instability in dry eye.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 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 | 0.001 |
| 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".