Application of Recommendations Regarding the Use of Subcutaneous Tumor Necrosis Factor Inhibitors in Spondyloarthritis by Rheumatologists in Daily Practice
Bibliographic record
Abstract
OBJECTIVE: To assess the implementation of European recommendations for use of TNF inhibitors for spondyloarthritis (SpA), rheumatologists' level of knowledge of and adherence to the recommendations, and potential barriers to the application of recommendations. METHODS: We conducted a retrospective study among 42 rheumatologists who initiated a first subcutaneous TNF inhibitor for SpA in 2013 or 2014. Thirty items from national and international recommendations were separated into 3 domains: indication, pretherapeutic monitoring, and management under TNF inhibitors. A standardized data collection procedure was used to gather data from medical files to assess the application of each recommendation. Questionnaires assessing the knowledge, level of adherence to each recommendation, and potential barriers to their implementation were sent to rheumatologists. RESULTS: Rheumatologists applied a mean of 60% of items from domains A and B, but less than 50% from domain C items. Recommendations regarding the search for previous infection and the prevention of future infections were the ones most often applied. However, < 60% of rheumatologists assessed cancer and other diseases before TNF inhibitor initiation. More than 95% of rheumatologists knew of the recommendations and had a high level of adherence. Lack of time, difficulties accessing specialized consultations, and lack of flexibility in the recommendations explained rheumatologists' difficulties in applying the recommendations. CONCLUSION: Despite high levels of knowledge of, and adherence to, recommendations for using TNF inhibitors for SpA, rheumatologists' application was limited because of a lack of human and material resources.
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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 | 0.016 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".