Bulbar Redness and Dry Eye Disease: Comparison of a Validated Subjective Grading Scale and an Objective Automated Method
Bibliographic record
Abstract
SIGNIFICANCE: In this study, assessments of conjunctival redness were performed to evaluate whether patients with or without dry eye disease (DED) could be discriminated based on this measure. Our findings suggest that subjectively grading redness by quadrant, as opposed to automated en face measurements, may be more suitable for this purpose. PURPOSE: This study aimed to quantify bulbar redness using the validated bulbar redness (VBR) grading scale and an automated objective method (Oculus Keratograph 5M; K5M) in participants with DED and non-DED controls. METHODS: Participants with DED (Ocular Surface Disease Index score ≥20 and Oxford scale corneal staining ≥2) and controls (Ocular Surface Disease Index score ≤10 and corneal staining ≤1) attended two study visits. In part 1A of visit 1, baseline bulbar redness was graded with the VBR scale in each conjunctival quadrant of both eyes, followed by automated measurements of temporal and nasal redness with the K5M. This was immediately followed by part 1B, during which a topical vasoconstrictor was instilled into both eyes. Redness assessments were repeated 5 and 30 minutes after instillation with both instruments. Participants returned 14 days later for visit 2, where the same assessments as for visit 1A were repeated. RESULTS: Seventy-four participants (50 DED and 24 controls) completed the study. There were statistically significant differences in redness between the DED and control groups when assessed with the VBR scale (14/16 comparisons; all, P < .05), whereas no significant differences in K5M-derived redness between the DED and non-DED groups were found at any location or time point. Both subjective and objective instruments detected statistically significant reductions in redness 5 and 30 minutes after instillation of the vasoconstrictor (all, P < .01). CONCLUSIONS: Although both subjective and objective instruments were sensitive to detecting changes in redness induced by vasoconstriction, statistically significant differences in redness between DED and control groups were only found using the VBR scale.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".