Sex- and Gender-Dependent Differences in Clinical and Preclinical Sepsis
Bibliographic record
Abstract
ABSTRACT: In this mini-review we provide an overview of sex- and gender-dependent issues in both clinical and preclinical sepsis. The increasing recognition for the need to account for sex and gender in biomedical research brings a unique set of challenges and requires researchers to adopt best practices when conducting and communicating sex- and gender-based research. This may be of particular importance in sepsis, given the potential contribution of sex bias in the failures of translational sepsis research in adults and neonates. Clinical evidence of sex-dependent differences in sepsis is equivocal. Since clinical studies are limited to observational data and confounded by a multitude of factors, preclinical studies provide a unique opportunity to investigate sex differences in a controlled, experimental environment. Numerous preclinical studies have suggested that females may experience favorable outcomes in comparison with males. The underlying mechanistic evidence for sex-dependent differences in sepsis and other models of shock (e.g., trauma-hemorrhage) largely centers around the beneficial effects of estrogen. Other mechanisms such as the immunosuppressive role of testosterone and X-linked mosaicism are also thought to contribute to observed sex- and gender-dependent differences in sepsis. Significant knowledge gaps still exist in this field. Future investigations can address these gaps through careful consideration of sex and gender in clinical studies, and the use of clinically accurate preclinical models that reflect sex differences. A better understanding of sex-and gender-dependent differences may serve to increase translational research success.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".