Guidelines for the Management of Complications of Diabetes in Saudi Arabia Using Delphi Technique for Consensus Among National Experts
Bibliographic record
Abstract
(1) Background: Saudi Arabia has one of the leading cases of diabetes globally, with approximately 27.8% of adults suffering from the disease. Given the negative consequences of diabetes mellitus (DM), it is critical to develop guidelines for its management. (2) Methods: After a thorough review of the literature around diabetes management, a diverse panel of 14 clinical experts was identified to participate in the Delphi process. The Delphi process included three rounds to ensure all available evidence was accounted for. (3) Results: The Delphi method concluded with a total of 37 guidelines reviewed and approved by the panelists, followed by verification from a third party in Saudi Arabia. The Delphi and external evaluation confirmed that authentic, relevant, and applicable evidence for diabetes management in Saudi Arabia was accounted for. The process concluded with a list of 37 statements about the management of acute and chronic complications of diabetes in Saudi Arabia. (4) Conclusions: The preparation of contextual evidence for the management of diabetes in Saudi Arabia will be instrumental in addressing the burden of disease in the region. The guidelines offer useful insights into diabetes care, especially by prioritizing early detection and proactive management of complications. They highlight the importance of lifestyle changes and medical therapy. However, due to the ever-changing nature of diabetes, the document must be monitored and updated on a regular basis to ensure its continued relevance and effectiveness.
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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 | 0.112 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".