Exploring Pediatric Tele-Rheumatology Practices During COVID-19: A Survey of the PRCOIN Network
Bibliographic record
Abstract
Healthcare providers were rapidly forced to modify the way they practiced medicine during the coronavirus disease 2019 (COVID-19) pandemic. Many providers transitioned from seeing their patients in person to virtually using telemedicine platforms with limited training and experience using this medium. In pediatric rheumatology, this was further complicated as musculoskeletal exams typically require hands-on assessment of patients. The objective of this study was to examine the adoption of telemedicine into pediatric rheumatology practices, to assess its benefits and challenges, and to gather opinions on its continued use. A survey was sent to the lead representatives of each Pediatric Rheumatology Care and Outcomes Improvement Network (PR-COIN) site to collect data about their center's experience with telemedicine during the COVID-19 pandemic. Quantitative data were analyzed using descriptive statistics, and qualitative data were thematically analyzed. Responses were received from the majority [19/21 (90%)] of PR-COIN sites. All respondents reported transitioning from in-person to primarily virtual patient visits during the COVID-19 pandemic. All centers reported seeing both new consultations and follow-up patients over telemedicine. Most centers reported using both audio and video conferencing systems to conduct their telemedicine visits. The majority of respondents [13/19 (68%)] indicated that at least 50% of their site's providers consistently used pediatric Gait Arms Legs and Spine (pGALS) to perform active joint count assessments over telemedicine. Over half of the centers [11/19 (58%)] reported collecting patient-reported outcomes (PROs), but the rate of reliably documenting clinical components varied. A few sites [7/19 (37%)] reported performing research-related activity during telemedicine visits. All centers thought that telemedicine visits were able to meet providers' needs and support their continued use when the pandemic ends. Benefits reported with telemedicine visits included convenience and continuity of care for families. Conversely, challenges included limited ability to perform physical exams and varying access to technology. Pediatric rheumatology providers were able to transition to conducting virtual visits during the COVID-19 pandemic. Healthcare providers recognize how telemedicine can enhance their practice, but challenges need to be overcome in order to ensure equitable, sustainable delivery of quality and patient-centered care.
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How this classification was reachedexpand
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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| 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, unvalidatedMachine predicted; a candidate call from one teacher head, 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".