Catching on to CATCH (Canadian Early Arthritis Cohort) Rheumatoid Arthritis Research: Translating 15 Years of Knowledge for Clinicians
Bibliographic record
Abstract
Objectives Since its inception in 2007, the Canadian Early Arthritis Cohort (CATCH) has produced hundreds of research outputs on rheumatoid arthritis (RA) pathogenesis, prognosis, comparative effectiveness, safety, quality improvement, patient and physician outcome measures, and comorbidities. Dissemination of knowledge from observational cohort studies is a critical step in the knowledge to action cycle but is often overlooked outside of formal academic publications and presentations.[1] Methods We developed a multi-modal targeted knowledge translation (KT) plan for clinicians focused on key learnings over the past 15 years. We first synthesized over 15 years of CATCH data into 15 actionable insights, each accompanied by a concise summary. Short videos were created for each learning, featuring members of the Scientific Advisory Committee and facilitated by an individual living with RA. These videos focused on describing the learning and practical ways to implement the insights into clinical practice with an emphasis on improving patient care. Infographic was also developed to complement the video series. Both the videos and the infographic were uploaded to YouTube and the CATCH website ( www.earlyarthritis.ca ) and shared on social media platforms, like X and Instagram under the handle @earlyarthritis. We refined the communications strategy by adjusting interview questions and updating keywords to better suit a clinician audience. All website content and some social media and video content were made available in French. Results The top 15 learnings from CATCH are summarized in Figure 1. Over 20 videos were filmed about the “Top 15 CATCH Learnings” series and will be released shortly on the CATCH YouTube channel (@canadianearlyarthritiscoho928). All videos are available in English, while 5 are available in French to meet the educational needs of francophone clinicians. The effectiveness of the KT strategy will be assessed using engagement metrics (eg, YouTube video views) and content reception measured via a survey. To date, the YouTube channel hosts over 140 videos available for patients and clinicians in English and French, with nearly 115,000 total views. Conclusion Implementing KT strategies is essential for bridging the evidence-to-practice gap in RA care.[2,3] Our clinician-focused KT strategy supports professional development and informs clinical practice. Future efforts will concentrate on assessing the effectiveness and impact of this KT strategy on clinician behavior, clinical practice, and patient outcomes. [1.] Straus SE. Defining knowledge translation. CMAJ 2009;181(3-4): 165-8. [2.] Brophy J. The Journal of Rheumatology 2016;43(6):1121-9. [3.] Grimshaw JM. Implement Sci 2012;7:50.
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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 | 0.048 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".