Diabetic retinopathy clinical practice guidelines: Customized for Iranian population
Bibliographic record
Abstract
PURPOSE: To customize clinical practice guidelines (CPGs) for management of diabetic retinopathy (DR) in the Iranian population. METHODS: Three DR CPGs (The Royal College of Ophthalmologists 2013, American Academy of Ophthalmology [Preferred Practice Pattern 2012], and Australian Diabetes Society 2008) were selected from the literature using the AGREE tool. Clinical questions were designed and summarized into four tables by the customization team. The components of the clinical questions along with pertinent recommendations extracted from the above-mentioned CPGs; details of the supporting articles and their levels of evidence; clinical recommendations considering clinical benefits, cost and side effects; and revised recommendations based on customization capability (applicability, acceptability, external validity) were recorded in 4 tables, respectively. Customized recommendations were sent to the faculty members of all universities across the country to score the recommendations from 1 to 9. RESULTS: Agreed recommendations were accepted as the final recommendations while the non-agreed ones were approved after revision. Eventually, 29 customized recommendations under three major categories consisting of screening, diagnosis and treatment of DR were developed along with their sources and levels of evidence. CONCLUSION: This customized CPGs for management of DR can be used to standardize the referral pathway, diagnosis and treatment of patients with diabetic retinopathy.
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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 | 0.050 | 0.178 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".