Anti-Interleukin 1 Treatment for Patients with Familial Mediterranean Fever Resistant to Colchicine: Table 1.
Bibliographic record
Abstract
OBJECTIVE: Familial Mediterranean fever (FMF) is a recessively inherited autoinflammatory disorder characterized by recurrent attacks of fever and serositis. Although colchicine is the standard therapy for preventing attacks and suppressing inflammation, 5%-10% of compliant patients are colchicine-resistant. We report the effect of anti-tumor necrosis factor therapy (etanercept) and anti-interleukin 1 (IL-1) treatment (anakinra) in 6 cases resistant to colchicine therapy. METHODS: Five children and an adult patient (3 female, 3 male) who were experiencing at least 2 attacks per month and had consistently elevated C-reactive protein levels despite regular colchicine therapy were given either etanercept or anakinra. RESULTS: Although etanercept lowered the number of attacks (from 3-4 attacks per month to 2 attacks per month), attacks still recurred and acute-phase reactants remained high in 2 patients; thus etanercept was considered ineffective. All 4 patients were switched to anakinra. In 2 patients anakinra completely resolved clinical and laboratory findings. The other 4 patients have been switched to anakinra recently; to date anakinra has reduced the number of attacks (to < 1 per month) and lowered the levels of acute-phase reactants. CONCLUSION: In this small series, anakinra was successful in suppressing inflammation and decreasing the number of attacks in FMF. This may be explained by the role of pyrin in the regulation of IL-1ß activation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".