Canadian Rheumatology Association Living Guidelines for Rheumatoid Arthritis: Update #1
Bibliographic record
Abstract
We have updated the Canadian Rheumatology Association (CRA) guidelines for rheumatoid arthritis, with a series of best practice statements and a recommendation for the choice of disease-modifying antirheumatic drug therapy after an inadequate response to tumor necrosis factor inhibitors (TNFi).These add to our prior recommendation for tapering of advanced therapy. 1 The full list of best practice statements and treatment recommendations is summarized (Table ).Readers should always consult the online version of the guideline, 2 which will always be the latest version with all recommendations, and include important contextual information for each recommendation, along with supporting evidence summaries.The online version is available via an interactive web-based platform for guideline authoring and publication (MAGICapp) and can be accessed directly (https://app.magicapp.org/#/guideline/jNxw7n)or through the CRA website (www.rheum.ca).The recommendations and statements were developed using the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach.3 Consistent with CRA processes, a full evidence-to-decision framework that summarizes the evidence and rationale for the recommendation is available for each treatment recommendation in the online version of the guideline.For the best practice statements, we present an explicit rationale for each statement following GRADE guidance, 4 also available in the online version of the guideline.We will continue to develop recommendations over time, and these will be added to the online version of the guideline on MAGICapp as they are developed.Journal versions of the guidelines will be published periodically to aid in knowledge translation.When citing the guidelines, both the original journal publication and online version should be cited, as these describe the full methods of development.
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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.049 | 0.029 |
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".