The Conversion of Bulbar Redness Grades Using Psychophysical Scaling
Bibliographic record
Abstract
PURPOSE: To use psychophysical scaling to investigate if the inclusion of reference anchors affected the perceived redness of the reference images of four bulbar redness grading scales and to convert grades between scales. METHODS: Ten participants were asked to arrange printed copies of the McMonnies/Chapman-Davies (6), IER (4), and Efron (5) grading scale images relative to each other, using the stationary but unlabeled 10, 30, 50, 70, and 90 reference images of the validated bulbar redness scale as additional anchors within a given 0 (minimum) to 100 (maximum) redness range (anchored scaling). The position of each image was averaged across observers to represent its perceived redness within this range. Anchored scaling data were then compared with data from a previous study, where the images of all four grading scales had been scaled for the same experimental setup, but with no reference anchors provided (non-anchored scaling). Averaged perceived redness as determined with anchored scaling was used to cross-calibrate grades between scales. RESULTS: Overall, perceived redness of the reference images was significantly different within each scale (repeated measures analysis of variance, all scales p < 0.001). There were differences in perceived redness range and when comparing reference levels between scales. Anchored scaling resulted in an apparent shift to lower perceived redness for all but one reference image compared with non-anchored scaling, with the rank order of the 20 images for both procedures remaining fairly constant (Spearman's ρ = 0.99). CONCLUSIONS: The re-scaling of the reference images in the anchored scaling experiment suggests that redness was assessed based on within-scale characteristics and not using absolute redness scores, a mechanism that can be referred to as clinical scale constancy. The perceived redness data allow practitioners to modify the grades of the scale they commonly use for comparison of their grading estimates with grades obtained with another calibrated scale.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".