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 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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".