Relationship between Corneal Sensation, Blinking, and Tear Film Quality
Bibliographic record
Abstract
PURPOSE: To examine the possible role of corneal sensitivity and tear film quality in triggering a blink by investigating the relationship between blink rate, central corneal sensitivity threshold (CST), ocular surface temperature (OST), tear meniscus height (TMH), tear film quality (noninvasive tear break-up time [NIBUT]), and tear film lipid pattern under normal conditions. METHODS: Forty-two volunteers (average age, 27.76 ± 5.36 years; 11 males) with good ocular health (Ocular Surface Disease Index, <15.0) were recruited for this cross-sectional cohort study. Blink rate, CST (noncontact corneal air gas aesthesiometry, NCCA), minimum and maximum OST in the central and inferior cornea between blinks (thermal infrared camera), TMH, NIBUT, and lipid pattern of the tear film (Keeler Tearscope Plus) were recorded on the right eye only. RESULTS: Median blink rate was 11 blinks/min (interquartile range [IR], 6.95 to 17.05), CST was 0.35 mbars (IR, 0.30 to 0.40), minimum OST in the central cornea was 35.15°C (IR, 34.58 to 35.50), and NIBUT was 34.55 s (IR, 12.45 to 53.80). Moderate but statistically significant correlations were observed between CST and NIBUT (r = 0.535, p < 0.001), CST and blink rate (r = -0.398, p < 0.001), lipid pattern and OST (r = 0.556, p < 0.001), and between CST and OST (r = 0.371, p = 0.008). The correlations between blink rate and NIBUT (r = -0.696, p < 0.001) and between OST and NIBUT (r = 0.639, p < 0.001; Spearman test) achieved higher significance; this was highlighted by the linear regression model where NIBUT and minimum central and inferior OST were identified as significant predictor variables. CONCLUSIONS: There is strong evidence for significant interactions between corneal sensitivity, NIBUT, OST, and blink frequency, emphasizing that ocular surface conditions represent a possible important trigger for the initiation of a blink. However, the mechanisms involved in the initiation of a blink are complex, with local ocular sensory input as only one trigger, along with other external influences and internal factors under cortical control.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".