Repeatability of Cone Contrast Color Vision Tests
Bibliographic record
Abstract
INTRODUCTION: New computerized color vision tests are gaining popularity in the aviation community. These tests determine color vision status by measuring chromatic sensitivity and they can effectively classify color vision as normal vs. abnormal. However, little information is available regarding their repeatability. We evaluated the repeatability of two such tests: the Operational Based Visual Assessment Cone Contrast Test (OCCT) and the Rabin Cone Contrast Test (RCCT). METHODS: A total of 56 subjects with normal color vision and 63 subjects with defective color vision completed both tests twice over 2 sessions. We determined the repeatability for a normal/abnormal result, between-eye differences in thresholds within a session, and between-session results for each eye. RESULTS: Both tests had excellent repeatability for normal vs. abnormal color vision (i.e., using a cutoff score of 75 Rabin Color Contrast Sensitivity Units). The OCCT also had excellent repeatability for acceptable vs. unacceptable color discrimination (i.e., a cutoff score of 55), whereas the RCCT repeatability was lower. The RCCT's lower repeatability was because the between-eye and between-session Limits of Agreement for the color-defective subjects were approximately ±40 relative sensitivity units. In contrast, the Limits of Agreement for the OCCT ranged from ±10 to ±15. DISCUSSION: These results reinforce the advantage of using a finer stimulus change when estimating cone thresholds in the clinical setting. Hovis JK, Almustanyir A, Glaholt M. Repeatability of cone contrast color vision tests. Aerosp Med Hum Perform. 2025; 96(4):287-295.
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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 | 0.006 | 0.019 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".