Specificity and sensitivity of Heidelberg retina tomograph macular edema maps and the effect of exudate and hemorrhage
Bibliographic record
Abstract
Abstract Purpose To assess the specificity and sensitivity of the Macular Edema Module (MEM) of the Heidelberg Retina Tomograph (HRT, Heidelberg Engineering, Heidelberg, Germany) versus Stereoscopic Color Fundus Photographs (SCFP) for patients with different grades of diabetic macular edema (ME) and non‐diabetic subjects. The effect of local exudates and hemorrhage on the specificity and sensitivity of MEM was also investigated. Methods The study included 20 eyes of 20 normal subjects without diabetes and 60 eyes of 60 diabetic patients. Eyes of diabetic patients were classified as without ME (n = 20), with questionable ME (n =20) and with Clinically Significant Macular Edema (CSME, n =20). All participants underwent a full ophthalmological evaluation, plus SCFP and HRT II MEM assessment. The sectors with exudate and/or hemorrhage on the SCFP were removed from the CSME group to generate a modified group (n=20) with CSME and without exudate or hemorrhage. Results In the normal subject group, the specificity of MEM was 92.2%. In the diabetes without macular edema group, the specificity of MEM was 91.7%. In the questionable ME group, the specificity of MEM was 93.1%. In the CSME group, the specificity of MEM was 87.3%, and the sensitivity for detecting CSME was 77.1%. In the modified group with CSME and without exudate or hemorrhage, the specificity of MEM was 85.7% and the sensitivity for detecting CSME was 96.9%. Conclusion The determination of diabetic macular edema by MEM shows good to moderate sensitivity and very good specificity. Furthermore, removing the influence of the exudate and/or hemorrhage resulted in excellent sensitivity and very good specificity.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".