[Improved diabetes care in Saxony Anhalt--results of the evaluation of the diabetes model project (first quarter 2001-last quarter 2002)].
Bibliographic record
Abstract
Approximately 5-6% of the German adult population suffers from diabetes, and the disease prevalence is expected to increase in the future. On the other hand, epidemiological studies show that early diagnosis, qualified training, and individualised therapy increase the quality of life of the patients and decrease the costs of treatment. A diabetes disease management trial was conducted in Saxony Anhalt in the years 2001 and 2002. The programme objective was to improve quality and efficiency of diabetes care. The study assessed the effects of a managed care approach, which included treatment corridors, patient education, and documentation of medical findings. A total of 19,957 patients and 263 physicians participated in the trial. The results of the evaluation show a professional and continuous supervision by physicians taking part in the programme. In comparison to the control group, patients enrolled in the plan displayed to a larger extent stabilised or lower (in some cases normalised) diabetes parameters, i.e., HbA1c levels, blood pressure, occurrence of hypoglycemia. During the trial phase, the total spending for plan participants was 0.85% lower than for non-programme patients. Lower primary care costs were overcompensated by reduced in-patient spending. The cost saving can be attributed, among others, to fewer hospitalisations and inpatient services for the treatment of diabetes.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".