An International Delphi Survey for the Definition of New Classification Criteria for Familial Mediterranean Fever, Mevalonate Kinase Deficiency, TNF Receptor–associated Periodic Fever Syndromes, and Cryopyrin-associated Periodic Syndrome
Bibliographic record
Abstract
OBJECTIVE: Provisional evidence-based classification criteria for hereditary periodic fever (HPF) have been recently developed. However, no consensus on how to combine clinical criteria, laboratory tests, and results of molecular analysis has been reached. The objective of this study is to understand which variables physicians consider important for the classification of patients with HPF. METHODS: Two Delphi surveys were sent to health professionals in the field of autoinflammation. In the first open survey, 124 researchers could list all the variables they consider useful for the diagnosis of each monogenic periodic fever. The variables could be of any type and each researcher could complete the survey for 1 or more diseases. In the second survey, 162 researchers were asked to select, from a list of items coming from the first survey, the 10 top variables and to rank them by assigning a score from 10 to 1. RESULTS: The response rates to the Delphi surveys were 85% for the first session and 87% for the second. The variables selected for each disease (corresponding to the third quartile, considering the total score obtained by the variables after the second Delphi survey) were 21 for mevalonate kinase deficiency, 22 for cryopyrinopathies, 18 for familial Mediterranean fever, and 20 for tumor necrosis factor receptor-associated periodic fever syndrome. A positive genetic test reached the top rank in all the HPF. CONCLUSION: Our process led to the identification of those features considered the most important as candidate variables to be included in a new set of evidence-based classification criteria for HPF.
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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 | 0.074 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".