Minimally disruptive medicine: how mHealth strategies can reduce the work of diabetes care
Bibliographic record
Abstract
Diabetes is a chronic metabolic disease in which the body has trouble regulating blood sugar due to a lack of insulin production by the pancreas (Type I diabetes) or by a resistance to the insulin that is produced (Type II diabetes). Over time, elevated levels of blood sugar (glucose) can cause serious damage to the heart, blood vessels, eyes, kidneys and nerves. The global prevalence of diabetes is currently 8.5% (up from 4.8% in 1980) or 422 million adults worldwide and is expected to continue increasing as the world's population ages. In the United States, the prevalence is slightly higher: 30.3 million people (or 9.4% of the general population) had diabetes in 2015, but this is a problem that gets worse with age: an estimated 25.2% of adults over 65 in the United States are diabetic. European rates of Type II diabetes range from 2.4% in Moldova to 14.9% in Turkey, with an estimated rate of undiagnosed diabetes in high-income European countries (Denmark, Finland, and the United Kingdom) of a staggering 36.6%. Although the rate of new diagnoses remains steady in higher income countries, diabetes prevalence continues to rise in low- and middle-income countries. Unfortunately, the WHO reports that 1.5 million deaths were directly attributable to diabetes in 2012, and a further 2.2 million deaths were caused by higher than optimal blood glucose, which caused death by cardiovascular and other related diseases. As a result, diabetes is one of four priority noncommunicable diseases targeted for action by world leaders.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.036 | 0.015 |
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".