An audit of the quality of care indicators for the management of diabetes in family practice clinics in Karachi, Pakistan.
Bibliographic record
Abstract
BACKGROUND: Management of diabetes is a painstaking and careful approach. This study was aimed to evaluate the quality of care for the management of diabetes provided by family practitioners to their patients having diabetes. This is a retrospective audit of medical records conducted in a tertiary care teaching hospital of private sector in Karachi for one month. METHODS: For this study, 150 medical records of patients with type 2 diabetes that visited family practice clinics for their diabetes care were examined. A total of 88 patient's medical records were selected and analyzed who attended the studied clinics for at least one year and had minimum of four out-patient visits. Majority (68%) of the audited medical records were of females. RESULTS: Of the total medical records analyzed, only one-quarter of the cases qualified the criteria of 'excellent' or 'good' diabetes care. Monitoring of body weight of the patient was only one indicator which was according the recommendations in 100% case at every visit. The other nearest quality of care indicator documented was blood glucose advice at every visit in 79.5% (95% CI: 71.1-87.9) of cases. Physical activity advised/reinforced at every visit was least observed (27.3%; 95% CI: 18.0-36.6). In addition, blood sugar control was reported in less than a quarter (23.9%) with 95% CI of 15.0-32.8. CONCLUSION: This work has identified a big gap in the management of type 2 diabetes provided by family practitioners. In addition, majority of the patients found to have poor glycemic control. Interventions are suggested to improve the quality of diabetes care. More such audits and research are recommended at the larger scale.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".