Effectiveness of interventions to increase uptake and completion of treatment for diabetic retinopathy in low- and middle-income countries: a rapid review protocol
Bibliographic record
Abstract
Abstract Background Vision loss due to diabetic retinopathy can largely be prevented or delayed through treatment. Patients with vision-threatening diabetic retinopathy are typically offered laser or intravitreal injections which often require more than one treatment cycle. However, treatment is not always initiated, or it is not completed, resulting in poor visual outcomes. Interventions aimed at improving the uptake or completion of treatment for diabetic retinopathy can potentially help prevent or delay visual loss in people with diabetes. Methods We will search MEDLINE, Embase, Global Health and Cochrane Register of Studies for studies reporting interventions to improve the uptake of treatment for diabetic retinopathy (DR) and/or diabetic macular oedema (DMO), compared with usual care, in adults with diabetes. The review will include studies published in the last 20 years in the English language. We will include any study design that measured any of the following outcomes in relation to treatment uptake and completion for DR and/or DMO: (1) proportion of patients initiating treatment for DR and/or DMO among those to whom it is recommended, (2) proportion of patients completing treatment for DR and/or DMO among those to whom it is recommended, (3) proportion of patients completing treatment for DR and/or DMO among those initiating treatment and (4) number and proportion of DR and/or DMO rounds of treatment completed per patient, as dictated by the treatment protocol. For included studies, we will also report any measures of cost-effectiveness when available. Two reviewers will screen search results independently. Risk of bias assessment will be done by two reviewers, and data extraction will be done by one reviewer with verification of 10% of the papers by a second reviewer. The results will be synthesised narratively. Discussion This rapid review aims to identify and synthesise the peer-reviewed literature on the effectiveness of interventions to increase uptake and completion of treatment for DR and/or DMO in LMICs. The rapid review methodology was chosen in order to rapidly synthesise the available evidence to support programme implementers and policy-makers in designing evidence-based health programmes and public health policy and inform the allocation of resources. Systematic review registration OSF osf.io/h5wgr
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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 | 0.034 | 0.056 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.021 | 0.019 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.050 | 0.004 |
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".