Use of the Auto-inflammatory Disease Activity Index to monitor disease activity in patients with colchicine-resistant Familial Mediterranean Fever, Mevalonate Kinase Deficiency, and TRAPS treated with canakinumab
Bibliographic record
Abstract
OBJECTIVES: To evaluate the feasibility of the autoinflammatory disease activity index (AIDAI) as a tool to assess disease activity in patients with hereditary recurrent fever syndromes (HRFs) treated with canakinumab. METHODS: Patients with active colchicine-resistant familial Mediterranean fever (crFMF), mevalonate kinase deficiency (MKD), or tumor necrosis factor receptor-associated periodic syndrome (TRAPS) were enrolled in the phase III CLUSTER study and asked to complete the AIDAI questionnaire daily. All patients included in the analysis were treated with canakinumab, but regimens and periods of treatment varied per study protocol. The AIDAI for each patient was calculated weekly over the first 40 weeks of study, based on the diaries completed over 30 days. Disease-specific cut-off AIDAI values for inactive disease were calculated in a ROC analysis by comparing AIDAI scores with the occurrence of clinically inactive disease, based on the physician global assessments of disease activity and the occurrence of flares. RESULTS: Sixty patients with crFMF, 70 with MKD, and 43 with TRAPS were included in the analysis. Median AIDAI scores were high during the first 4 weeks for the three disease cohorts, and decreased afterwards, with some differences between disease cohorts. AIDAI values of 12.0, 9.6 and 15.5 were obtained as the most optimal thresholds to discriminate patients with inactive disease, with sensitivity and specificity values mostly over 75%. CONCLUSIONS: The AIDAI allows to discriminate between patients with active and inactive HRFs, and can be used in clinical practice to monitor the disease course of patients and the effect of medications.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".