Discus: Investigating Subjective Judgment of Optic Disc Damage
Bibliographic record
Abstract
PURPOSE: To describe a software package (Discus) for investigating clinicians' subjective assessment of optic disc damage [diagnostic accuracy in detecting visual field (VF) damage, decision criteria, and agreement with a panel of experts] and to provide reference data from a group of expert observers. METHODS: Optic disc images were selected from patients with manifest or suspected glaucoma or ocular hypertension who attended the Manchester Royal Eye Hospital. Eighty images came from eyes without evidence of VF loss in at least four consecutive tests (VF negatives), and 20 images from eyes with repeatable VF loss (VF positives). Software was written to display these images in randomized order, for up to 60 s. Expert observers (n = 12) rated optic disc damage on a 5-point scale (definitely healthy, probably healthy, not sure, probably damaged, and definitely damaged). RESULTS: Optic disc damage as determined by the expert observers predicted VF loss with less than perfect accuracy (mean area under receiver-operating characteristic curve, 0.78; range, 0.72 to 0.85). When the responses were combined across the panel of experts, the area under receiver-operating characteristic curve reached 0.87, corresponding to a sensitivity of ∼60% at 90% specificity. Although the observers' performances were similar, there were large differences between the criteria they adopted (p < 0.001), even though all observers had been given identical instructions. CONCLUSIONS: Discus provides a simple and rapid means for assessing important aspects of optic disc interpretation. The data from the panel of expert observers provide a reference against which students, trainees, and clinicians may compare themselves. The program and the analyses described in this article are freely accessible from http://www.discusproject.blogspot.com/.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".