Sexual dimorphism in immunity across animals: a meta‐analysis
Bibliographic record
Abstract
In animals, sex differences in immunity are proposed to shape variation in infection prevalence and intensity among individuals in a population, with females typically expected to exhibit superior immunity due to life-history trade-offs. We performed a systematic meta-analysis to investigate the magnitude and direction of sex differences in immunity and to identify factors that shape sex-biased immunocompetence. In addition to considering taxonomic and methodological effects as moderators, we assessed age-related effects, which are predicted to occur if sex differences in immunity are due to sex-specific resource allocation trade-offs with reproduction. In a meta-analysis of 584 effects from 124 studies, we found that females exhibit a significantly stronger immune response than do males, but the effect size is relatively small, and became non-significant after controlling for phylogeny. Female-biased immunity was more pronounced in adult than immature animals. More recently published studies did not report significantly smaller effect sizes. Among taxonomic and methodological subsets of the data, some of the largest effect sizes were in insects, further supporting previous suggestions that testosterone is not the only potential driver of sex differences in immunity. Our findings challenge the notion of pervasive biases towards female-biased immunity and the role of testosterone in driving these differences.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.017 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".