The Interblink Interval I: The Relationship between Sensation Intensity and Tear Film Disruption
Bibliographic record
Abstract
PURPOSE: To find the relationship between tear film drying and sensation during the interblink period. METHODS: One eye was taped shut, and after a blink the subjects were required to keep the other eye open. Digital video images of the ocular surface (with fluorescein) were obtained using a slit lamp biomicroscope while 23 subjects rated the intensity of the ocular surface sensation by adjusting a one-turn potentiometer to represent the strength of the sensation. They were trained to use the potentiometer before the data were collected. In addition, the characteristics of the sensation as spoken by the subject were recorded. RESULTS: The sensation was generally triphasic, with initial constant sensation and a subsequent biphasic period, with intensity increasing slowly followed by a rapid increase before the subjects blinked (correlations were all r > 0.95). Tear film drying dynamics were also biphasic, and drying and sensation were strongly associated, with a correlation of 0.94 between the break in the bilinear functions of sensation and drying. CONCLUSIONS: The method provides novel information about the development of ocular sensation during ocular surface drying. As evidenced by the complex functions required to adequately describe the relationships, tear film drying and ocular surface sensations are associated in complex ways.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".