Agreement of the Heidelberg Retina Tomograph II Macula Edema Module With Fundus Biomicroscopy in Diabetic Maculopathy
Bibliographic record
Abstract
OBJECTIVES: To estimate the agreement between the macular edema maps (MEMs) of the Retina Module of the Heidelberg Retina Tomograph II (Heidelberg Engineering, Heidelberg, Germany) and contact lens fundus biomicroscopy (FB) and to assess the influence of combining MEM data with the results of short-wavelength automated perimetry (SWAP) and fluorescein angiography (FA) on diagnostic test performance. DESIGN: Prospective, observational case series. METHODS: Twenty patients (20 eyes) with diabetic retinopathy with or without clinically manifest macular edema (11 and 9 eyes, respectively) were enrolled. All patients underwent full ophthalmologic examination and also MEM assessment, SWAP, and FA. RESULTS: Using FB as the "gold standard," the agreement between the MEMs and FB was very good (Kendall coefficient of concordance, 0.80). Macular edema maps showed good agreement with FA and SWAP (Kendall coefficient, 0.64 and 0.65). Virtually all of the edematous areas detected with MEM but not seen clinically had decreased sensitivity on SWAP and/or fluorescein leakage. CONCLUSIONS: Macular edema maps demonstrated very good agreement with FB. Combining the results of FA and SWAP with those of the MEMs provided supporting evidence of concomitant blood-retinal barrier leakage and visual dysfunction, respectively, in areas of early retinal thickening. Prospective studies are ongoing to fully assess the diagnostic test performance of MEMs in the detection of early and progressive diabetic macular edema.
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 | 0.008 | 0.026 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".