Optical Coherence Tomography Angiography Changes in Diabetic Macular Ischemia after Systemic Normobaric Oxygen Therapy
Bibliographic record
Abstract
Purpose: To evaluate vascular changes on optical coherence tomography angiography (OCTA) in patients with diabetic macular ischemia (DMI) after systemic normobaric oxygen (NBO) therapy. Methods: This before–after interventional study included 26 eyes of 26 patients with DMI. Macular OCTA was performed before and after 1 hour of 100% NBO therapy at a flow of 10 L/min delivered by face mask. As primary outcomes, changes in OCTA metrics were evaluated using the paired t-test. Subgroup analyses were performed based on gender. The secondary outcomes included identifying parameters correlated with best-corrected visual acuity (BCVA) and factors associated with improvement in OCTA parameters. Results: The patients included 15 males and 11 females aged 59.48 ± 9.67 years. Overall, no significant change was observed in retinal thickness; however, there was a significant decrease in retinal thickness among females and a significant increase among males (P < 0.001). The foveal avascular zone (FAZ) decreased significantly from 0.38 ± 0.14 to 0.34 ± 0.12 mm2 (P = 0.035). Superficial capillary plexus vessel density (SCP-VD) and deep capillary plexus vessel density (DCP-VD) at fovea increased from 13.5 ± 6.37 to 14.98 ± 6.33% (P = 0.059) and from 24.61 ± 6.75 to 26.59 ± 6.16% (P = 0.022), respectively. In males, BCVA correlated significantly with baseline DCP parameters but corresponded with none of the SCP parameters. In females, BCVA significantly correlated with pre-O2 DCP-VD of the perifoveal inferior quadrant. Finally, regression analysis did not show any parameter that could predict a favorable response. Conclusion: Using OCTA, we observed a decrease in FAZ and an increase in DCP-VD at fovea after short-term NBO therapy for patients with DMI.
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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 | 0.000 | 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".