The Perceived Bulbar Redness of Clinical Grading Scales
Bibliographic record
Abstract
PURPOSE: To use a psychophysical scaling method to estimate the perceived redness of reference images of the McMonnies and Chapman-Davies (six reference levels), Institute for Eye Research (four), Efron (five), and Validated Bulbar Redness (five) bulbar redness grading scales. METHODS: Regions of interest were cropped out of the grading scale reference images; three separate image sets (color, grayscale, and binarized) were created for each scale, combining to a total of 20 images per image set. Ten naïve observers were asked to arrange printed copies of the 20 images per image set across a distance of 1.5 m on a flat surface, so that separation reflected their perception of bulbar redness; only start and end point of this range were indicated. The position of each image was averaged across observers to represent the perceived redness for this image, within the 0 to 100 range. Subjective data were compared with physical attributes (chromaticity and spatial metrics) of redness. RESULTS: For each image set, perceived redness of the reference images within each scale was ordered as expected, but not all consecutive within-scale levels were rated as having different redness. Perceived redness of the reference images varied between scales, with different ranges of severity being covered by the images. Perception of redness severity depended on the image set (repeated-measures analysis of variance; all p < or = 0.0002). The perceived redness was strongly associated with the physical attributes of the reference images. CONCLUSIONS: Subjective estimates of redness are based on a combination of chromaticity and vessel-based components. Psychophysical scaling of perceived redness lends itself to being used to cross-calibrate these four clinical scales.
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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 | 0.003 | 0.022 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".