The global challenges and opportunities in the practice of rheumatology: White paper by the World Forum on Rheumatic and Musculoskeletal Diseases
Bibliographic record
Abstract
Rheumatic and musculoskeletal diseases (RMDs) represent a multitude of degenerative, inflammatory and auto-immune conditions affecting millions of people worldwide. Persons with these diseases may potentially experience severe chronic pain, joint damage, increasing disability and even death. With an increasingly ageing population, the prevalence and burden of RMDs are predicted to increase, placing greater demands on the global practice of rheumatology and related healthcare budgets. Effective treatment of RMDs currently faces a number of challenges in both the developed and developing world, and individual countries may face more specific local challenges. However, limited understanding of the burden of RMDs amongst public health professionals and policy-makers means that these diseases are often not considered a public health priority. The objective of this review is to increase awareness of the RMDs and to identify opportunities to address RMD challenges on both a local and global scale. On 26 September 2014, rheumatology experts from five different continents met at the World Forum on Rheumatic and Musculoskeletal Diseases (WFRMD) to discuss and identify some key challenges for the RMDs community today. The outcomes are presented in this review, focusing on access to rheumatology services, diagnostics and therapies, rheumatology education and training and on clinical trials, as well as investigator-initiated and epidemiological research. The long-term vision of the WFRMD is to increase perception of the RMDs as a major burden to society and to explore potential opportunities to improve global and local RMD care.
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 | 0.016 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".