Wide-field optical coherence tomography imaging in diabetic retinopathy
Bibliographic record
Abstract
PURPOSE: To report the individual retinal layer thicknesses up to mid-equator in patients with diabetic retinopathy (DR) using Spectralis (Heidelberg Engineering, Heidelberg, Germany) wide-field optical coherence tomography (OCT). METHODS: Retinal layers were segmented using a custom designed semi-automated algorithm, where reference points were marked by the examiner to enable software to automatically compute the thickness values of each retinal sublayer at an interval of 1 mm from reference points. The values of individual retinal thicknesses in eyes with varying severity of DR were compared with the values of healthy subjects. Generalized estimating equation was performed to compensate for inclusion of both eyes of patients. RESULTS: A total of 64 patients (119 eyes) with a mean age of 68.97 ± 10.27 years were included. Overall, ganglion cell layer (GCL)/ inner plexiform layer (IPL) complex (-31.67 microns, p < 0.001), outer plexiform layer (-6.78 microns, p = 0.002) and photoreceptor layer (-22.90 microns, p < 0.001) showed significant thinning, while outer nuclear layer thickening ( + 68.19 microns, <0.001) was noted in eyes with DM compared to healthy subjects. Thickness changes were significantly more in the macular segment compared to nasal and temporal segments. GCL/ IPL complex and photoreceptor layers were found to be significantly thin in all grades of DR. CONCLUSION: Retinal thicknesses vary significantly in patients with diabetic retinopathy and understanding patterns of these changes across different segments of the wide field OCT may help better elucidate the natural progression of the disease in terms of retinal anatomy.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| 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".