A Clinical Criterion to Exclude the Hyperimmunoglobulin D Syndrome (Mild Mevalonate Kinase Deficiency) in Patients with Recurrent Fever
Bibliographic record
Abstract
OBJECTIVE: The hyperimmunoglobulin D syndrome (HIDS) is an autosomal recessive autoinflammatory disease caused by mutations in the mevalonate kinase gene. Our objective was to define a clinical criterion able to exclude HIDS without the need of genetic testing. METHODS: A recursive partitioning algorithm was applied to derive the clinical criterion in 149 patients with genetic testing in a French laboratory, among whom 35 had HIDS. The criterion was validated in 93 patients with genetic testing in a Dutch laboratory, among whom 28 had HIDS. RESULTS: The most discriminatory composite clinical criterion satisfied by all patients with HIDS in the derivation group was [onset age < 5 years old OR (joint pain during attacks AND length of attacks < 14 days)]. It had a sensitivity of 100% (95% confidence interval 88% to 100%) and a specificity of 28% (95% CI 17% to 40%) in the validation group. If genetic testing had been limited to patients fulfilling this criterion, 18 tests (19%) would have been avoided in this highly selected validation sample, without missing a single patient with HIDS. CONCLUSION: Even among patients already selected by expert physicians, this criterion could help prevent unnecessary genetic testing, which is resource- and time-consuming.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".