Principles of Software Engineering for the Cost-Effective Prevention of Type 2 Diabetes (T2D)
Bibliographic record
Abstract
Diabetes is growing increasingly prevalent as a result of changes in people's diets and lifestyles, in addition to increases in income. Self-care on the part of diabetic patients is an essential component of diabetes management. The ability of patients to self-manage their conditions can benefit significantly by assistance provided by other patients. To that aim, the purpose of this paper is to study how Software Engineering and Software Reuse can improve the quality of care as well as the cost-effectiveness of treatment for chronic diseases in Canada, namely Type-2 Diabetes (T2D). In the Hispanic and Latino adolescent population of Sacramento County, the objective of the health education programme known as “Hidratación Saludable” is to bring the rate of the development of type 2 diabetes down to a more manageable level. Teenagers are the beneficiaries, while their parents will make up the audience for this particular message. Finally, the software that was developed was put to use to simulate a randomized controlled trial (RCT), which was done in order to compare the cost-effectiveness of two preventative programs-gym incentive programmers and diabetes prevention programs-against a control group that did not take part in any preventative programmer.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".