Characteristics and Treatment Patterns of Patients with Diabetic Macular Edema Non-Responsive to Anti-Vascular Endothelial Growth Factor Treatment in Ontario, Canada
Bibliographic record
Abstract
Purpose: To understand the demographics, clinical characteristics, treatment patterns, visual and anatomic responses of patients with diabetic macular edema (DME) initially treated with anti-vascular endothelial growth factor (anti-VEGF) agents in the real-world clinical setting. Patients and Methods: This retrospective cohort study used electronic health records to identify consecutively presenting patients with DME who received their first documented anti-VEGF injection (index injection) on or after 1 October 2015 and before 30 September 2016 (index period) at 4 clinical sites in Ontario, Canada. Patients receiving anti-VEGF injections in the study eye were followed for ≥18 months. After the first 3 monthly injections, patients were classified as "responder" (≥20% reduction in central retinal thickness [CRT] from index date) or "nonresponder" (<20% reduction in CRT) to anti-VEGF treatment. Results: At 12 months, change from baseline (CFB) in best visual acuity (BVA) of responders (n = 30) was mean (SD) 12.8 (13.00) letters; CFB in nonresponders (n = 56) was 3.2 (16.3) letters. Sensitivity analyses stratified by initial BVA were supportive. Mean (SD) change in CRT (μm) was -160.4 (111.4) in responders and -62.2 (98.6) in nonresponders. While changes in anti-VEGF therapy were lower in responders versus nonresponders (10.0% vs 23.2%), mean number of injections was similar (8.3 in each cohort). Conclusion: Despite receiving a substantial number of injections and requiring changes in therapy more frequently, nonresponders showed a lack of clinically meaningful change in BVA and CRT. Nonresponders could be identified after 3 anti-VEGF injections. There remains an unmet need for treatment options in patients with DME who show a nonresponse after 3 months of anti-VEGF treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".