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
Introduction Arthritis and rheumatic conditions (i.e., arthritis) are responsible for major health care expenditures and disability burdens. The impact of arthritis is not restrained by national boundaries. It is one of the most prevalent chronic conditions and is a leading cause of disability in Australia (1), Canada (2,3), Europe (4), the United Kingdom (5), and the United States (6,7), affecting an estimated 3 million Australians, 6 million Canadians, 8 million in the UK, almost 43 million people in the US, and 103 million across Europe. With the aging of the baby boomers, these numbers and the associated disabilities will quickly escalate. By 2020 in the US alone, arthritis is projected to affect 60 million people, and the activities of 12 million people may be limited by arthritis (6). The growing magnitude of people affected by arthritis motivates the need to review what is known about its national costs to identify areas where current information is lacking. In addition, it is important to determine targets for public health efforts that will reduce the costs of and burden from arthritis. This knowledge will facilitate planning research agendas that support informed public policy decisions.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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