Improving type 2 diabetes mellitus management in Ministry of Defense Hospitals in the Kingdom of Saudi Arabia 2018–2021
Bibliographic record
Abstract
Diabetes mellitus is a metabolic disease characterised by elevated levels of blood glucose and is a leading cause of disability and mortality. Uncontrolled type 2 diabetes leads to complications such as retinopathy, nephropathy and neuropathy. Improved treatment of hyperglycaemia is likely to delay the onset and progression of microvascular and neuropathic complications.This article describes the efforts of 18 governmental hospitals in the Kingdom of Saudi Arabia that enrolled in a collaborative improvement project to improve the poor glycaemic control (HbA1c >9% to be less than 15%) of patients with diabetes by the end of 2021 among all the chronic illness clinics in the enrolled military hospitals. Enrolled hospitals were required to implement an evidence-based change package that included the implementation of diabetes clinical practice guidelines with standardised assessment and care planning tools. Furthermore, care delivery was standardised using a standard clinic scope of service that focused on multidisciplinary care teams. Finally, hospitals were required to implement diabetes registries that were used by case managers for poorly controlled patients.The project timetable was from October 2018 to December 2021. Diabetes poor control (HbA1c >9%) showed improved mean difference of 12.7% (34.9% baseline, 22.2% after) with a p value of 0.01. Diabetes optimal testing significantly improved from 41% at the start of the project in the fourth quarter of 2018, reaching 78% by the end of the fourth quarter of 2021. Variation between hospitals showed a significant reduction in the first quarter of 2021.The collaborative multilevel approach of standardising the care based on the best available evidence through policies, guidelines and protocols, patient-focused care and integrated care plan by a multidisciplinary team was associated with noticeable improvement in all key performance indicators of the project.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".