Translating research into practice—implementation recommendations for pediatric rheumatology; Proceedings of the childhood arthritis and rheumatology research alliance 2020 implementation science retreat
Bibliographic record
Abstract
The translation of research findings into clinical practice is challenging, especially fields like in pediatric rheumatology, where the evidence base is limited, there are few clinical trials, and the conditions are rare and heterogeneous. Implementation science methodologies have been shown to reduce the research- to- practice gap in other clinical settings may have similar utility in pediatric rheumatology. This paper describes the key discussion points from the inaugural Childhood Arthritis and Rheumatology Research Alliance Implementation Science retreat held in February 2020. The aim of this report is to synthesize those findings into an Implementation Science Roadmap for pediatric rheumatology research. This roadmap is based on three foundational principles: fostering curiosity and ensuring discovery, integration of research and quality improvement, and patient-centeredness. We include six key steps anchored in the principles of implementation science. Applying this roadmap will enable researchers to evaluate the full range of research activities, from the initial clinical design and evidence acquisition to the application of those findings in pediatric rheumatology clinics and direct patient 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 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 | 0.019 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.011 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| 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".