Severe infectious diseases of childhood as monogenic inborn errors of immunity
Bibliographic record
Abstract
This paper reviews the developments that have occurred in the field of human genetics of infectious diseases from the second half of the 20th century onward. In particular, it stresses and explains the importance of the recently described monogenic inborn errors of immunity underlying resistance or susceptibility to specific infections. The monogenic component of the genetic theory provides a plausible explanation for the occurrence of severe infectious diseases during primary infection. Over the last 20 y, increasing numbers of life-threatening infectious diseases striking otherwise healthy children, adolescents, and even young adults have been attributed to single-gene inborn errors of immunity. These studies were inspired by seminal but neglected findings in plant and animal infections. Infectious diseases typically manifest as sporadic traits because human genotypes often display incomplete penetrance (most genetically predisposed individuals remain healthy) and variable expressivity (different infections can be allelic at the same locus). Infectious diseases of childhood, once thought to be archetypal environmental diseases, actually may be among the most genetically determined conditions of mankind. This nascent and testable notion has interesting medical and biological implications.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".