French recommendations for the management of Behçet’s disease
Bibliographic record
Abstract
Behçet's disease (BD) is a systemic variable vessel vasculitis that involves the skin, mucosa, joints, eyes, arteries, veins, nervous system and gastrointestinal system, presenting with remissions and exacerbations. It is a multifactorial disease, and several triggering factors including oral cavity infections and viruses may induce inflammatory attacks in genetically susceptible individuals. BD vasculitis involves different vessel types and sizes of the vascular tree with mixed-cellular perivascular infiltrates and is often complicated by recurrent thrombosis, particularly in the venous compartment. Several new therapeutic modalities with different mechanisms of action have been studied in patients with BD. A substantial amount of new data have been published on the management of BD, especially with biologics, over the last years. These important therapeutic advances in BD have led us to propose French recommendations for the management of Behçet's disease [Protocole National de Diagnostic et de Soins de la maladie de Behçet (PNDS)]. These recommendations are divided into two parts: (1) the diagnostic process and initial assessment; (2) the therapeutic management. Thirty key points summarize the essence of the recommendations. We highlighted the main differential diagnosis of BD according to the type of clinical involvement; the role of genetics is also discussed, and we indicate the clinical presentations that must lead to the search for a genetic cause.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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".