Diversity of malignancies in patients with different types of inborn errors of immunity
Bibliographic record
Abstract
Genetic defects in the development, maturation, and/or function of the immune cells can lead to Inborn errors of immunity (IEI) which may predispose patients to malignancies. The overall risk for cancer in children with IEI ranges from 4 to 25% and the type of malignancy is highly dependent on the specific mutant gene underlying IEI. We investigated 3056 IEI patients registered in the Iranian national registry between the years 1999 and 2020 in this retrospective cohort study. The frequency of malignancy and its association with the type of IEI in these patients were evaluated. A total of 82 IEI patients with malignancy were enrolled in this study. Among them, predominantly lymphoma was the most common type of malignancy (67.1%), followed by leukemia (11%), and cancers of the head and neck (7.3%). Among identified lymphoma cancers, non-Hodgkin's lymphomas were the most frequent type (43.9%) followed by different subtypes of Hodgkin's lymphoma (23.2%). Solid tumors (18.3%) appeared to be very heterogeneous by type and localization. The correlation between the type of malignancy and survival status and the association between the type of malignancy and IEI entities were unremarkable. The awareness of the association between the presence of IEI and cancer highlights the importance of a synergistic effort by oncologists and immunologists in the early diagnosis of malignancy and personalized therapeutic strategies in IEI patients.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".