Reconciling Mouse and Human Immunology at the Altar of Genetics
Bibliographic record
Abstract
Immunity to infection has been extensively studied in humans and mice bearing naturally occurring or experimentally introduced germline mutations. Mouse studies are sometimes neglected by human immunologists, on the basis that mice are not humans and the infections studied are experimental and not natural. Conversely, human studies are sometimes neglected by mouse immunologists, on the basis of the uncontrolled conditions of study and small numbers of patients. However, both sides would agree that the infectious phenotypes of patients with inborn errors of immunity often differ from those of the corresponding mutant mice. Why is that? We argue that this important question is best addressed by revisiting and reinterpreting the findings of both mouse and human studies from a genetic perspective. Greater caution is required for reverse-genetics studies than for forward-genetics studies, but genetic analysis is sufficiently strong to define the studies likely to stand the test of time. Genetically robust mouse and human studies can provide invaluable complementary insights into the mechanisms of immunity to infection common and specific to these two species.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| 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".