The Effects of Increasing Ocular Surface Stimulation on Blinking and Sensation
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to determine how increasing ocular surface stimulation affected blinking and sensation, while controlling task concentration. METHODS: Ten healthy subjects concentrated on a task while a custom pneumatic device generated air flow toward the central cornea. Six flow rates (FRs) were randomly presented three times each and subjects used visual analog scales to record their sensory responses. The interblink interval (IBI) and the FR were recorded simultaneously and the IBI, sensory response, and corresponding FR were determined for each trial. The FR associated with a statistically significant decrease in IBI, the blink increase threshold (BIT), was calculated for each subject. RESULTS: Both the mean and SD of IBI were decreased with increasing stimulation, from 5.69 ± 3.96 seconds at baseline to 1.02 ± 0.37 seconds at maximum stimulation. The average BIT was 129 ± 20 mL/min flow rate with an IBI of 2.33 ± 1.10 seconds (permutation test, P < 0.001). After log transformation, there was a significant linear function between increasing FR and decreasing IBI within each subject (Pearson's r ≤ -0.859, P < 0.05). The IBI was highly correlated with wateriness, discomfort, and cooling ratings (Pearson's r ≤ -0.606, P < 0.001). CONCLUSIONS: There was a dose-response-like relationship between increased surface stimulation and blinking in healthy subjects, presumably for protection of the ocular surface. The blink response was highly correlated with ocular surface sensation, which is not surprising given their common origins. The BIT, a novel metric, may provide an additional end point for studies on dry eye or other conditions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".