Minimally disruptive medicine: how mHealth strategies can reduce the work of diabetes care
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it