Comparative analysis of global practices in the management of colchicine-resistant familial Mediterranean fever: a CliPS network analysis
Bibliographic record
Abstract
BACKGROUND: Although colchicine is the mainstay of familial Mediterranean fever (FMF) treatment, 5-10% of patients are considered to have colchicine resistance (CR). However, there is no globally agreed CR definition or indications for biological disease-modifying anti-rheumatic drugs (bDMARDs). METHODS: A survey on 'Biologics in Monogenic Autoinflammatory Diseases', part of the 'Clinical Practice Strategies' (CLiPS) initiative, was conducted by a JIR cohort-initiated eCOST network among expert participants worldwide. Our primary aim was to provide a flowchart reflecting the different CR definitions and present data regarding bDMARD indications. The secondary aim was to determine how specific biases influence clinical approaches. We analysed the CliPS according to the experience levels of physicians, country-specific FMF prevalence, countries' gross domestic product, bDMARD availability and reimbursement policies of the countries. RESULTS: A total of 223 responses from 46 countries were included in the study. Almost half of the respondents (73/160, 45.6%) indicated that three to four attacks within the preceding 6 months were necessary for their CR definition. The most frequently used acute-phase reactant was C-reactive protein (157/164, 95.7%). Almost three-fourths of the respondents (74%, n=165) considered that supplementary factors, including complications of FMF, attack severity, elevated activity scores, patient-reported outcome and quality of life scales, influenced their CR definition. CONCLUSION: We present a novel flowchart describing physicians' general attitudes and unique findings regarding management strategies for colchicine-resistant FMF and shifting trends influenced by epidemiological and socioeconomic factors.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".