Improving Clinical Management of Diabetic Macular Edema: Insights from a Global Survey of Patients, Healthcare Providers, and Clinic Staff
Bibliographic record
Abstract
INTRODUCTION: In contrast with patients receiving therapy for retinal disease during clinical trials, those treated in routine clinical practice experience various challenges (including administrative, clinic, social, and patient-related factors) that can often result in high patient and clinic burden, and contribute to suboptimal visual outcomes. The objective of this study was to understand the challenges associated with clinical management of diabetic macular edema from the perspectives of patients, healthcare providers, and clinic staff, and identify opportunities to improve eye care for people with diabetes. METHODS: We conducted a survey of patients with diabetic macular edema, providers, and clinic staff in 78 clinics across 24 countries on six continents, representing a diverse range of individuals, healthcare systems, settings, and reimbursement models. Surveys comprised a series of single- and multiple-response questions completed anonymously. Data gathered included patient personal characteristics, challenges with appointment attendance, treatment experiences, and opportunities to improve support. Provider and clinic staff surveys asked similar questions about their perspectives; and clinic characteristics were also captured. RESULTS: Overall, 5681 surveys were gathered: 3752 from patients with diabetic macular edema, 680 from providers, and 1249 from clinic staff. Too many appointments, too short treatment intervals, difficulties in traveling to the clinic or arranging adequate support to travel, out-of-pocket costs, office/parking fees, and long waiting times were noted by all as contributing to increase the burden on the patient and caregiver. Patients generally desired more in-depth discussions with their provider, which would help with information exchange and better expectation-setting. CONCLUSIONS: The wealth of systematic data generated by this global survey highlights the breadth and scale of challenges associated with the clinical management of patients with diabetic macular edema. Addressing the opportunities for improvement raised by patients, providers, and clinic staff could increase patient adherence to treatment, reduce appointment burden, and improve clinic capacity.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".