Arthritis and rheumatism are neglected health priorities: a bibliometric study.
Bibliographic record
Abstract
OBJECTIVE: To investigate the frequency of publications about arthritis and rheumatic diseases relative to other diseases and to examine which topics received most attention. METHODS: Available health statistics were used to quantify the burden of illness due to musculoskeletal (MSK) conditions. Next, a bibliographic analysis of MEDLINE was performed comparing disease categories using the MeSH tree structure for 1991 and 1996. Diseases were ranked according to the frequency of citations attributable to them and further analyses were performed for journal categories, MeSH subheadings, and the frequency of citations for specific types of arthritis and rheumatic diseases. RESULTS: Compared with 9 other causes, MSK diseases are leading contributors to health professional consultations, total health costs, chronic ill health, and disability. In contrast, MSK diseases ranked ninth among twelve major MEDLINE disease categories in 1996 and 1991. These rankings were similarly low across journal categories reflecting basic science research and clinical application. Radiography, rehabilitation, history and embryology were the most frequently used subheadings for MSK diseases. In 1996, there were 16,603 citations for MSK diseases, led by bone diseases (7,304 citations), joint diseases (4,987), muscular diseases (4,236), arthritis (3,555), and rheumatic diseases (3195). Among arthritic and rheumatic diseases, rheumatoid arthritis had the largest number of citations (2,004), followed by systemic lupus erythematosus (927) and osteoarthritis (793). CONCLUSION: Arthritis and rheumatic diseases receive far less attention in the scientific literature than is warranted by their enormous and growing disease burden. Both research and dissemination are lacking and more adequate resources for these activities are indicated.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.014 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
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".