Complexity in unclassified auto-inflammatory disease: a case report illustrating the potential for disease arising from the allelic burden of multiple variants
Bibliographic record
Abstract
BACKGROUND: Despite recent advances in the diagnosis and understanding of many autoinflammatory diseases, there are still a great number of patients with phenotypes that do not fit any clinically- and/or genetically-defined disorders. CASE PRESENTATION: We describe a fourteen-year-old boy who presented at two and a half years of age with recurrent febrile episodes. Over the course of the disease, the episodes increased in frequency and severity, with new signs and symptoms continuing to appear. Most importantly, these included skin changes, splenomegaly and transaminitis. Only partial control of the disease was achieved with anti-IL-1 therapy. Extensive investigation showed generalized inflammation without immune deficiency, with increased levels of serum amyloid A and several pro-inflammatory cytokines including interferon-γ, as well as an increased type I interferon score. Exome sequence analysis identified P369S and R408Q variants in the MEFV innate immunity regulator, pyrin (MEFV) gene and T260 M and T320 M variants in the NLR family pyrin domain containing 12 (NLRP12) gene. CONCLUSION: Patients with unclassified and/or unexplained autoinflammatory syndromes present diagnostic and therapeutic challenges and collectively form a substantial part of every cohort of patients with autoinflammatory diseases. Therefore, it is important to acquire their full genomic profile through whole exome and/or genome sequencing and present their cases to a broader audience, to facilitate characterization of similar patients. A critical mass of well-characterized cases will lead to improved diagnosis and informed treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".