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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".