Quality improvement of diabetes care using flow sheets in family health practice
Bibliographic record
Abstract
OBJECTIVE: To show that the use of a flow sheet would improve performance of family physicians in diabetes care. METHODS: This is a one-year intervention study conducted in 7 family practice clinics in Taif Armed Forces Hospitals, Taif, Saudi Arabia from March 2006 to June 2007. Diabetic flow sheet was developed based on the clinical practice guidelines of Canada for the management of type 2 diabetes. Patients' records were selected by systematic random sampling technique. RESULTS: Four hundred and fourteen medical records of patients with type 2 diabetes were included in the study. Compliance with the quality indicators was audited using 9 quality improvement indicators. Significant improvement was detected in the indicators of body mass index, glycosylated hemoglobin, microalbuminuria, lipid profile, retinoscopy, foot examination, and peripheral neuropathy examination. CONCLUSION: Flow sheet can be effective in improving quality of care not only for diabetes but also for other chronic conditions.
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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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".