Reduction of complications generated by Type 2 Diabetes Mellitus using a remote health care solution in Peru
Bibliographic record
Abstract
In this article, we propose a technological architecture that supports the remote medical care service for elderly people suffering from Type 2 Diabetes Mellitus in Peru. The problem is based on the limitations that elderly people have with compliance with medical controls, which leads to increased complications of the disease and the patient's quality of life. The design of the technological architecture is based on 6 layers: 1) devices, 2) software, 3) channels, 4) data storage, 5) data processing and 6) information visualization. Through the solution, patients are able to autonomously manage their disease through periodic glucose control and the execution of an updated treatment in real time. In the same way, the specialist doctor periodically analyzes the glucose level and it is notified in real time about the out-of-range indicators, which allows him to make treatment decisions as the anomaly occurs. The focus of the solution is to reduce the complications generated by the disease through efficient glucose control and periodic medical advice. The solution was validated in a nursing home with adults over 60 and an endocrinologist from a medical office in Lima, Peru. For the study, we measured the constancy of the glucose record, the average response time of the doctor in case of emergencies or indicators out of range, the percentage of reduction of complications and the level of satisfaction of the Telehomecare solution in older adults. The results show that patients interact more frequently as they adapt the solution as part of their daily routine. On the other hand, the response time was approximately 4.13 minutes from the anomaly record. The percentage of complication reduction was 14% and the level of satisfaction of the solution was reflected in the dimensions of the response time and understanding of the user's need.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".