Enhancing type 2 diabetes treatment through digital plans of care. First results from the East Cheshire Study of an App to support people in the management of type 2 diabetes
Bibliographic record
Abstract
Introduction The use of personalised care planning has been effective at improving health outcomes for people with long-term health conditions. Methods We analysed data in relation to changes in BMI/HbA1c. The sample was made up of ( n = 36) participants randomised to either the active intervention group (App+usual care) or the control group (usual care). Results: The average HbA1c percentage change for the treatment group was 9.5%, but just −2% for the control (usual care) group ( P = 0.015 for the difference). The average percentage change in BMI for the treatment group was −0.4%, but 0.1% for the control group ( P = 0.03 for the difference). Conclusion These preliminary findings point to how the provision of personalised plans of care, support and education linked to a mobile app, can result in HbA1c and BMI reduction over a 6-month period. While the results are preliminary, they portend the potential for digital plans of care to enhance T2DM management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".