Pediatric rheumatology education: the virtual frontier a review
Bibliographic record
Abstract
BACKGROUND: Many children with rheumatic and musculoskeletal diseases are unrecognized. Identifying these children requires health care provider awareness, knowledge, and skills to recognize disease features and how (and when) to refer to specialist care. The aim of this paper is to highlight the need for better access to health care, review the essential role that education and virtual care play to address unmet need in low resource areas and especially to expand workforce capacity. Using collaborative partnerships, virtual platforms, and innovative assessment methods, musculoskeletal care and education can be delivered to reach a greater audience than ever before. Increased awareness through multiple initiatives and readily available resources are imperative to improve global rheumatology care. CONCLUSION: The needs of children with rheumatic diseases and musculoskeletal conditions are vastly underserved around the world resulting in preventable morbidity and mortality. Expanded implementation of virtual education and e-health care platforms provides an opportunity to increase access to care for children globally.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".