Conjunctival and Corneal Pneumatic Sensitivity Is Associated with Signs and Symptoms of Ocular Dryness
Bibliographic record
Abstract
PURPOSE: To investigate the relationships of dry eye symptoms and corneal and conjunctival sensitivity to pneumatic stimulation, tear film stability, and clinical ocular surface characteristics in symptomatic and asymptomatic subjects. METHODS: Ninety-seven subjects were enrolled and grouped by a questionnaire-based single score for symptoms of ocular dryness (none to trace, non-dry group; mild to severe, symptomatic group); 43 were symptomatic and 54 were non-dry. Corneal (K) and conjunctival (C) sensitivities were measured with a computer-controlled Belmonte pneumatic (room temperature) stimulus. Symptoms were assessed according to the Ocular Surface Disease Index (OSDI). Ocular surface staining with fluorescein (FL) and lissamine green (LG), noninvasive tear film break-up time (NIBUT), and the phenol red thread test (PRT) were assessed. RESULTS: The symptomatic group showed lower K and C thresholds (P < 0.01), greater corneal FL and conjunctival LG staining, and shorter NIBUT than did the non-dry eye group (all others P < 0.05). The OSDI scores were higher in the symptomatic group (P < 0.001). K and C thresholds and NIBUT were inversely correlated with the OSDI and corneal and conjunctival staining (all P < 0.05). The K and C threshold and NIBUT (all P < 0.01) correlated positively. Step-wise multiple regression analysis showed that ocular surface sensitivity and NIBUT were significant predictors of the OSDI. CONCLUSIONS: Ocular irritation assessed with the OSDI is associated with ocular surface hyperesthesia to cooling, corneal epitheliopathy, and tear film instability. Although cause and effect are unclear, the analysis showed that altered corneal and conjunctival sensory processing and tear film attributes are essential aspects of what characterizes dry eye.
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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 | 0.002 |
| 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 | 0.000 | 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".