A Comparison of the Glaucoma Probability Score to Earlier Heidelberg Retina Tomograph Data Analysis Tools in Classifying Normal and Glaucoma Patients
Bibliographic record
Abstract
PURPOSE: To compare the performance of the Glaucoma Probability Score (GPS), Mikelberg linear discriminant function (LDF), Burk LDF, and Moorfield regression analysis (MRA) in classifying optic disc images acquired from normals and glaucoma patients with the Heidelberg Retina Tomograph (HRT). PATIENTS AND METHODS: This is a retrospective comparative study of 110 eyes of 110 subjects clinically categorized as glaucoma or normal. Topographic images of the optic nerve head were obtained from HRT. Data analysis of the HRT images were carried out using GPS, Mikelberg LDF, Burk LDF, and MRA. Diagnostic performances and agreement in classification between the data analysis tools were calculated and compared for GPS, Mikelberg LDF, Burk LDF, and MRA. RESULTS: The highest specificity but lowest sensitivity value was obtained using the Burk LDF. The highest sensitivity but lowest specificity was obtained using the GPS. The GPS and MRA were however similar in performance. Sensitivity and specificity values of the GPS and other LDFs were affected by disc size. CONCLUSIONS: The GPS compared similarly with the MRA without the need of additional contour line placements. Disc size is still an important factor in classification by the GPS and the other LDFs.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".