Increasing access without compromising quality: optimizing telemedicine care in pediatric rheumatology
Bibliographic record
Abstract
Juvenile idiopathic arthritis (JIA) is a chronic immune-mediated condition affecting approximately 1 in 1,000 children in the United States. Without appropriate treatment, JIA can lead to permanent disability, chronic pain, and impaired quality of life. Targeted treatment strategies have enabled some patients to achieve sustained inactive disease and improved outcomes. However, the ongoing workforce shortage of pediatric rheumatologists poses a barrier to accessing effective care for many patients and families. Telemedicine is a potential solution to improving access to care for patients who travel significant distances to obtain pediatric rheumatology healthcare. During the COVID-19 pandemic, telemedicine saw rapid adoption with exponential uptake in use during unprecedented circumstances and resulted in inconsistent implementation of this emerging healthcare modality. While virtual visits offer many benefits, they often fail in gathering critical data elements required for measuring and tracking health outcomes, engaging in data-driven shared decision making, population health management and secondary research. In response to these challenges, the Pediatric Rheumatology Care and Outcomes Improvement Network (PR-COIN) Digital Health Workgroup conducted a needs assessment among its members to identify opportunities and barriers for optimizing telemedicine visits for patients with JIA. Based on this assessment, potential interventions and instructional materials were developed and compiled into a "change package" to assist rheumatology teams in planning, implementing and evaluating improvements to telemedicine visits at their centers. This change package is publicly available on the PR-COIN website for adoption and use (https://www.pr-coin.org/center-tools-resources). Continuous evaluation and adaptation of these tools are essential as telemedicine practices continue to evolve.
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 | 0.013 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".