Quality of Care for Patients with Type 2 Diabetes Mellitus in Dubai: A HEDIS-Like Assessment
Bibliographic record
Abstract
Objective. As little data are available on the quality of type 2 diabetes mellitus (T2DM) care in the Arabian Gulf States, we estimated the proportion of patients receiving recommended monitoring at the Dubai Hospital for T2DM over one year. Methods. Charts from 150 adults with T2DM were systematically sampled and quality of care was assessed during one calendar year, using a Healthcare Effectiveness Data and Information Set- (HEDIS-) like assessment. Screening for glycosylated haemoglobin (HbA1c), low-density lipoprotein (LDL), blood pressure, retinopathy, and nephropathy was considered. Patients were classified based on their most recent test in the period, and predictors of receiving quality care were examined. Results. Mean age was 58 years (standard deviation (SD): 12.4 years) and 33% were males. Over the year, 98% underwent HbA1c screening (50% had control and 28% displayed poor control); 91% underwent LDL screening (65% had control); 55% had blood pressure control; 30% had retinopathy screening; and 22% received attention for nephropathy. No individual characteristics examined predicted receiving quality care. Conclusion. Some guideline monitoring was conducted for most patients; and rates of monitoring for selected measures were comparable to benchmarks from the United States. Greater understanding of factors leading to high adherence would be useful for other areas of preventive care and other jurisdictions.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".