Relationship Between the Degree of Iris Pigmentation and Corneal Sensitivity to a Cooling Stimulus
Bibliographic record
Abstract
PURPOSE: To explore the relationship between the degree of iris pigmentation and corneal sensitivity threshold (CST) on a variety of different ethnicities, using the air-jet noncontact corneal aesthesiometer and by applying a consistent method of subject iris pigmentation classification. METHODS: A total of 200 subjects (mean age 23.7 ± 3.1 years, 127 women) participated in this clinical cross-sectional study: 100 whites, 40 Asians, 40 Chinese, and 20 Afro-Caribbeans. CST was assessed within the central cornea using a noncontact corneal aesthesiometer, and the degree of iris pigmentation of each subject was noted according to the Seddon method using a set of graded photographs of iris pigmentation (grades 1-5). Inclusion criteria were absence of ocular disease including dry eye, no contact lens wear, and no use of artificial tears. Statistical testing between ethnicities was made by the pairwise t test with Holm adjustment, and a linear model was set up to analyze the effects of ethnicity and iris grade. RESULTS: A moderate trend for increasing CST with increasing iris pigmentation grade for all ethnicities was observed (R = 0.46; P < 0.0001), with CST changing from 0.66 ± 0.16 mbars for grade 1, 0.74 ± 0.18 mbars for grade 2, 0.86 ± 0.31 mbars for grade 3, 0.85 ± 0.32 mbars for grade 4, and 1.08 ± 0.40 mbars for grade 5. This correlation was stronger within the white group, representing the only ethnicity with all iris pigmentation grades (R = 0.50; P < 0.0001). CONCLUSIONS: There is a moderate relationship between corneal sensitivity and the degree of iris pigmentation, with sensitivity increasing as iris pigmentation decreases. This relationship is stronger within whites.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".