The Use of Fluorescent Quenching in Studying the Contribution of Evaporation to Tear Thinning
Bibliographic record
Abstract
PURPOSE: The purpose of our study was to test the prediction that if the tear film thins due to evaporation, rather than tangential flow, a high concentration of fluorescein in the tear film would show a greater reduction in fluorescent intensity compared to a low concentration of fluorescein due to self-quenching at high concentrations. METHODS: Tear film thickness, thinning rate, and fluorescent intensity were measured continuously and simultaneously with a modified spectral interferometer in 30 healthy subjects with two different concentrations (2% followed by 10%) of 1 μL of liquid fluorescein on the eye. Measurements of fluorescein self-quenching (fluorescent efficiency as a function of fluorescein concentration) are described in an Appendix and are reported in arbitrary units. RESULTS: Under low and high fluorescein concentration conditions, there were no differences in tear film thickness (P = 0.09) or thinning rates (P = 0.76). While the mean initial fluorescent intensity was similar between groups (637.47 ± 381.47 vs. 672.09 ± 649.72, P = 0.55), the mean rate of fluorescent decay was 4-fold faster in the high (16.57 ± 29.34) than in the low (4.11 ± 6.78) concentration group (P < 0.01). CONCLUSIONS: The large difference in the rate of fluorescent decay between groups can be explained by the effects of evaporation and self quenching of fluorescein; the latter is expected to be greater for high than for low fluorescein concentration. Fluorescence decay due to tangential flow would be expected to be similar at high and low fluorescein concentrations. This supports previous evidence that evaporation has the primary role in normal tear thinning between blinks.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".