The Association Between Eye Color and Corneal Sensitivity Measured Using a Belmonte Esthesiometer
Bibliographic record
Abstract
PURPOSE: The purpose of this study is to determine the association between corneal sensitivity measured using a pneumatic esthesiometer and eye color quantified objectively. METHODS: Twenty subjects had ocular surface sensitivity measured using a Belmonte esthesiometer. An ascending method of limits followed by the method of constant stimuli were used to estimate 1) cold detection thresholds, 2) discomfort detection thresholds (both using pneumatic stimuli at 20 degrees C, 3) mechanical detection thresholds using pneumatic stimuli at 50 degrees C (ocular surface temperature approximately 33 degrees C), and 4) percent CO2 chemical detection thresholds using 50 degrees C pneumatic stimuli at flow rates set at half of each subject's pneumatic detection threshold (therefore detected by the chemical content and not the mechanical content). Eye color was estimated 1) clinically by two observers ranking the color (light to dark) of digital images of each subject's iris, 2) photometrically by measuring iris luminance, and 3) using chromaticity obtained from a Photo Research 650 spectroradiometer with controlled illumination. Correlation and linear and nonlinear regression analyses were used to examine relationships between variables. RESULTS: There were no associations between eye color (determined clinically or objectively) for mechanical and chemical detection thresholds (best r = 0.15, all p > 0.05). There was a significant linear association between 20 degrees detection thresholds and eye color (r = 0.39), which was substantially improved with a two-line function (part level and part increasing linearly, r = 0.65). CONCLUSIONS: We were generally unable to demonstrate the relationship between eye color and sensitivity reported previously using a Cochet-Bonnet esthesiometer. However, for a subset of subjects with palest irises, there appears to be a linear association between eye color and sensitivity to cooling stimuli.
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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 | 0.000 |
| 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) | 0.003 | 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".