Bibliographic record
Abstract
Doctors' visits for inflammatory arthritis reportedly represent only 6% of the overall visit rates for all arthritis and related conditions (163 per 1000), with about 40% of these patients seeing a specialist. Data from provincial drug plan databases show that although the proportion of the population aged 65 years and older with prescriptions for disease modifying antirheumatic drugs increased to 1% in 2000, this is only half the expected prevalence of rheumatoid arthritis in this age group. There are large provincial variations. Despite data on the efficacy and importance of treatment of early inflammatory arthritis, research is lacking on: the experience of arthritis, decision-making about seeking medical care, and factors affecting access to, and payment for, treatment, including drugs and rehabilitation; primary care decision-making about referral and treatment; organizational aspects of specialist care and access to drugs; and new ways of delivering services to reach patients in underserviced or remote areas. Monitoring the population impact of arthritis, including economic costs, is a priority for research, as are epidemiological studies on risk factors.
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.
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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.014 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".