Retrospektive Studie zur Validierung & Entwicklung einer objektiven Beurteilung des konjunktivalen Rötungsgrades mittels Videotopographen
Bibliographic record
Abstract
Purpose. The purpose of this exploratory study was to evaluate and validate the objective determination of bulbar, limbal and total conjunctival redness with the K5M video topographer (Oculus, Germany), the measurement software (R-Scan) and the JENVIS grading scale in comparison to subjectively recorded redness levels. Material and Methods. The conjunctival redness level was documented three consecutive times in a total of 75 subjects (150 eyes). The average age was 33.9 ± 15.7 years. The survey took place at the Centre for Contact Lens Research Waterloo, Canada and at the Helios hospital Erfurt, Germany. From the survey, 25 images were selected and entered into a website for subjective grading. On the website, the images were presented in a randomized order six times (3× limbal & 3× bulbar redness), so that each of the 20 investigators graded a total of 150 images. The reference images of the redness grades were based on the JENVIS grading scale. The subjective results were compared using the five analyzed grades with the objective findings by the R-Scan of the K5M. The results were analyzed using descriptive statistics. Results. With regard to the images obtained using the software, the mean grade of findings was 1.3 ± 0.3, with 24 % of the results being assessed as grade 0, 48 % as grade 1, 14 % as grade 2 and grade 3. The subjective findings resulted in a mean grade of 1.5 ± 0.3. 17 % were assessed as grade 0, 45 % as grade 1, 19 % as grade 2 and 5 % as grade 3. The results of the subjective and objective classification of temporal and nasal bulbar redness are not statistically different. In contrast, the results of temporal and nasal limbal redness are statistically different, with the subjective findings within grade 1 being statistically significantly higher than those of the objective findings. The mean dispersion of the objective classifications is 11 %, that of the subjective 31 %. Conclusion. The software (R-Scan, K5M) primarily evaluates the number and redness of the conjunctival vessels, whereas the subjective evaluation is based on the overall redness of the eye as perceived by the examiner. As the degree of severity increases, the spread of the results increases with objective classification, but decreases with subjective classification, with the latter showing an almost 3-times greater spread on average. This means that automated conjunctival classifications are more accurate than subjective classifications for lower degrees of severity and comparable for higher degrees of severity.
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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 | 0.054 | 0.133 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".