The Development of Validated Bulbar Redness Grading Scales
Bibliographic record
Abstract
ABSTRACT Purpose. To develop a perceptually and physically based bulbar redness grading scale. Methods. Digital conjunctival hyperemia photographs were taken using a photo‐slit lamp at controlled exposures. Nine participants arranged 25 images on a tabletop over a range of 1.5 m, using separation to represent changes in redness. The position of each image was recorded and normalized for a 0 to 100 scale, and compared to chromaticity of each image obtained using a spectrophotometer. The performance of two versions of the scale (5 and 10 images) and a continuous grading scale was evaluated based on repeatability data collected from nineteen observers who used each scale twice to grade 30 randomly presented images of bulbar redness. Results. Psychophysical scaling was highly correlated between single observers (Pearson's r ≥ 0.92, p < 0.05). The averaged subjective grades significantly correlated with chromaticity (r = 0.95 and r = 0.99, p < 0.001 for CIE u* and log u*, respectively). Across all observers, test and retest ratings were highly correlated with either scale (r ≥ 0.98), and showed high levels of repeatability expressed by intraclass correlation coefficients (ICC ≥ 0.98), correlation coefficients of concordance (CCC ≥ 0.96), and coefficients of repeatability (COR ≤ 5.64). Despite single unit increment options, the majority of grade values assigned using the discrete scales were distributed in multiples of 5. Conclusions. Combining psychophysical and physical attributes is a promising method for the development of novel anterior segment scales; the newly developed scales performed well in a clinical setting.
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 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 | 0.011 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".