Performance of Different Diagnostic Criteria for Familial Mediterranean Fever in Children with Periodic Fevers: Results from a Multicenter International Registry
Bibliographic record
Abstract
OBJECTIVE: Our aims were to validate the pediatric diagnostic criteria in a large international registry and to compare them with the performance of previous criteria for the diagnosis of familial Mediterranean fever (FMF). METHODS: Pediatric patients with FMF from the Eurofever registry were used for the validation of the existing criteria. The other periodic fevers served as controls: mevalonate kinase deficiency (MKD), tumor necrosis factor receptor-associated periodic syndrome (TRAPS), cryopyrin-associated periodic syndrome (CAPS), aphthous stomatitis, pharyngitis, adenitis syndrome (PFAPA), and undefined periodic fever from the same registry. The performances of Tel Hashomer, Livneh, and the Yalcinkaya-Ozen criteria were assessed. RESULTS: The FMF group included 339 patients. The control group consisted of 377 patients (53 TRAPS, 45 MKD, 32 CAPS, 160 PFAPA, 87 undefined periodic fevers). Patients with FMF were correctly diagnosed using the Yalcinkaya-Ozen criteria with a sensitivity rate of 87.4% and a specificity rate of 40.7%. On the other hand, Tel Hashomer and Livneh criteria displayed a sensitivity of 45.0 and 77.3%, respectively. Both of the latter criteria displayed a better specificity than the Yalcinkaya-Ozen criteria: 97.2 and 41.1% for the Tel Hashomer and Livneh criteria, respectively. The overall accuracy for the Yalcinkaya-Ozen criteria was 65 and 69.6% (using 2 and 3 criteria), respectively. Ethnicity and residence had no effect on the performance of the Yalcinkaya-Ozen criteria. CONCLUSION: The Yalcinkaya-Ozen criteria yielded a better sensitivity than the other criteria in this international cohort of patients and thus can be used as a tool for FMF diagnosis in pediatric patients from either the European or eastern Mediterranean region. However, the specificity was lower than the previously suggested adult criteria.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".