[Mapping of HbA1c as a quality measure in the primary care of diabetic patients in Thuringia, Germany].
Bibliographic record
Abstract
BACKGROUND: In order to improve quality and outcome in the primary care for patients with diabetes mellitus it is necessary to recognise districts with poorer supply of structured diabetes care. Hba1c is an evaluated surrogate parameter which indicates the long-term control of diabetes therapy. The aim of the study was to create a map with all Hba1c-values of every district of the Free State of Thuringia, Germany. METHODS: Medical laboratories working for general practitioners were used as a new data source. All hba1c-values which were measured in Thuringia within the 4th quarter of 1997 were collected. A corresponding postal code was registered for each hba1c-value for identification purposes. Thuringia consists of 23 districts, and this geographic structure was used as a basis for the hba1c-map. RESULTS: 6 laboratories sent 23,214 hba1c-values. 16,591 values belonged to the territory of Thuringia: The medium relative hba1c (hba1c/mean normal of healthy people) for Thuringia was 1.44, two-fold increased hba1c was found in 7.9% of all hba1c values. The proportion of two-fold increased hba1c-values varied up to 100%. CONCLUSION: This pilot study disclosed significant differences in the outcome of regional ambulatory care for patients with diabetes mellitus in Thuringia. An intervention in districts with poorer study outcomes might result in direct benefits for patients and providers of health care services. Since hba1c-values could not be collected for all districts the first hba1c-map remains incomplete.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".