Discriminant analysis of clinical color vision tests and color related tasks
Bibliographic record
Abstract
Purpose: Individuals with congenital color deficiencies show a large inter-observer variation in performing color related tasks. One such task is interpreting color codes used in VDT displays. The aim of this study is to determine if Ishihara, HRR plates, Farnsworth D-15, Adams D-15 and CN Lantern can predict the performance on a VDT color naming task. Methods: 100 color-normals and 52 color-defectives participated in the study. Pass/Fail criteria for the color naming task was established based on number and types of color normal errors. Results: Discriminant analysis was performed to compare the clinical tests with the task. HRR plates, Farnsworth D-15, Adams D-15 and CN Lantern were significant predictors according to the model. Of all the clinical tests, the Farnsworth D-15 test was the best predictor of performance on the practical test. The sensitivity and specificity was 0.75 and 0.92 respectively. Conclusions: Certain clinical tests are reasonable predictors for determining who can adequately identify certain VDT colors. These tests are generally used to diagnose moderate to severe color vision defects. The Farnsworth- D-15 test was the best predictor of the practical test. However, it was not perfect. Whether the Farnsworth- D-15 is sufficient for occupational purposes depends on the critical safety issues.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
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".