Changes in Corneal Detection Thresholds After Repeated Tear Film Instability
Bibliographic record
Abstract
Purpose: To use a human-based model to study the effects of repeated tear film instability on corneal detection thresholds to cold, mechanical, and chemical stimuli. Methods: Twenty-five subjects participated in three study visits. A computer-controlled Belmonte esthesiometer was used to estimate corneal detection thresholds to cold, mechanical, and chemical stimuli before, after, and 30 minutes following 10 consecutive sustained tear exposure (STARE) trials. Subjects turned a pain knob (0-10) to indicate discomfort during STARE trials. The area of tear breakup and thinning in each trial was analyzed. Symptoms were evaluated by the Current Symptom Questionnaire (CSQ). Results: There was a significant time effect on CSQ symptoms during both visits (Friedman test, P < 0.001), with immediately after repeated STARE and 30 minutes later significantly differing from before STARE (Wilcoxon, P < 0.017). Tear breakup occurred in every trial, ranging from 25% to 88% of the exposed corneal area and all subjects indicated discomfort during trials. There was a significant time effect on mechanical thresholds between before STARE mechanical thresholds and 30 minutes later (repeated measures analysis of variance [ANOVA] P < 0.001), but not cold (P = 0.057) or chemical (P = 0. 565) thresholds. Conclusions: In this study, tear breakup during STARE trials was associated with discomfort, which when repeated, resulted in increased symptoms of ocular discomfort and alterations of mechanical sensory thresholds after 30 minutes. These results suggest that tear film instability, which is thought to occur repeatedly during normal blinking among dry eye patients over the day, can produce neurosensory alterations.
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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 | 0.001 |
| 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".