Interventions to increase attendance for diabetic retinopathy screening: a systematic review and meta‐analysis
Bibliographic record
Abstract
Purpose Study objectives were to: 1) determine the effectiveness of interventions to improve diabetic retinopathy screening ( DRS ) attendance; 2) specify intervention content in terms of behaviour change techniques ( BCT s); 3) determine whether interventions that included particular BCT s were more effective in increasing attendance. Methods We searched the Cochrane Library, MEDLINE , EMBASE and clinical trials registers to February 2017 for randomised controlled trials ( RCT s) that were designed to improve attendance for DRS or were evaluating general quality improvement ( QI ) strategies for diabetes care and reported the effect of the intervention on DRS attendance. We did not use any date or language restrictions in the searches. We identified and categorised component BCT s using an established BCT Taxonomy ( BCTT v1). Results We included 66 RCT s. QI interventions were multifaceted and targeted patients, healthcare professionals ( HCP s) or healthcare systems. Overall, DRS attendance increased by 12% (risk difference ( RD ) 0.12 [95% CI 0.10‐0.14]) compared with usual care, with substantial heterogeneity in effect size. Both DRS ‐targeted and general QI interventions were effective, particularly where baseline DRS attendance was low. All frequently identified BCT s were associated with significant improvements in attendance. Higher effect estimates were observed in sub‐group analyses for the BCT s ‘goal setting (outcome)’ (0.26 [0.16‐0.36]) and ‘feedback on outcomes of behaviour’ ( RD 0.22 [0.15‐0.29]) in interventions targeting patients, and ‘restructuring the social environment’ ( RD 0.19 [0.12‐0.26]) and ‘credible source’ ( RD 0.16 [0.08‐0.24]) in interventions targeting HCP s. Conclusions RCT evidence indicates that QI interventions incorporating specific BCT components are associated with meaningful improvements in DRS attendance compared to usual care.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.013 | 0.008 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".