Augmenting the Referral Pathway for Retinal Services Among Patients With Diabetes Mellitus at Reiyukai Eiko Masunaga Eye Hospital, Nepal: Protocol for a Nonrandomized, Pre–Post Intervention Study
Bibliographic record
Abstract
BACKGROUND: Diabetic retinopathy (DR) is an important public health issue in Nepal with a huge social and economic impact. Despite the availability of retinal services, people may not access them because of the lack of knowledge about DR and poor referral systems. Published studies on referral pathways in Nepal are scarce. Improving DR awareness among general physicians has the potential to address these challenges. OBJECTIVE: The aim of this study is to evaluate the effect of a health education intervention on health personnel, establish a referral pathway, and assess the impact of the intervention on the attendance of patients with diabetes mellitus for retinal screening at Reiyukai Eiko Masunaga Eye Hospital in Nepal. METHODS: This is a nonrandomized, pre- and postintervention study. Health education on DR will be provided to selected health personnel of the intervention hospital (Scheer Memorial) using information education and communication (IEC) materials in the form of PowerPoint presentations, posters, pamphlets, videos, and pre- and postevaluation questionnaires along with referral slip. Pre- and postevaluation will be undertaken during the study period. Data will be analyzed using MS Excel and Epi Info 7. RESULTS: The ethical approval for this study has been obtained from the Ethical Review Board of the Nepal Health Research Council (ERB Protocol Registration Number # 582/2020P). The study is expected to be completed in 18 months from the start of the project. The baseline data collection was from June to January 2020 for a period of 8 months. The postintervention data collection was from February to September 2021 for a period of 8 months. The last 2 months are planned for data analysis and report writing. CONCLUSIONS: Health education intervention could be a low-cost solution to improve the awareness, access, and utilization of retinal health care services; this is an understudied topic in Nepal. Working closely with the stakeholders, this study will evaluate the role of health education interventions (which are already validated in other low-income settings) to strengthen referral and reduce the burden of DR in Nepal. TRIAL REGISTRATION: ClinicalTrials.gov NCT04829084; https://clinicaltrials.gov/ct2/show/NCT04829084. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/33116.
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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 | 0.024 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.044 | 0.008 |
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".