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
Type 2 diabetes is an increasingly common disease (1) that places a considerable economic burden on society. An estimated 171 million people were suffering from diabetes in 2000, and this number could total 366 million by 2030 (1). Type 2 diabetes accounts for more than 90% of all diabetes cases, and it often appears in middle age (2). In 2010, the prevalence of diabetes in the U.S. was 11.3 and 26.9% among individuals aged 20 years or over and 65 years or older (2), respectively. In 2007, costs related to diabetes in the U.S. were an estimated $174 billion; $116 billion in direct costs and $58 billion in indirect costs (3). Direct costs include the cost of personal expenditures, drugs, and health care services, whereas indirect costs include lost productivity at work. Lost productivity at work may be measured through absenteeism (time lost from work due to illness), presenteeism (time at work impaired due to illness), productivity (time lost from work due to illness plus time at work impaired due to illness), or early retirement (retirement before the official retirement age due to illness). Lost productivity at work is an important concern for employees, employers, and society. Moreover, the complications related to diabetes are a major cause of disability, reduced quality of life, and death (4). Employees with diabetes may stop working prematurely (5–8) and may experience unemployment (7,9–12), which could translate into a reduction in earned income and savings (13) and loss of self-esteem (14). For employers too, lost productivity due to absenteeism (6,8,13,15–23), presenteeism (17), and early retirement (5–7) is an important economic issue. To the best of our knowledge, there are no published systematic reviews answering the following question: Do individuals …
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.007 |
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