Personalized Treatment of Asthma: The Importance of Sex and Gender Differences
Bibliographic record
Abstract
An individual's sex (nominally male or female, based on biological attributes) and gender (a complex term referring to socially constructed roles, behaviors, and expressions of identity) influence the clinical course of asthma in several ways. The physiologic development of the lungs and effects of sex hormones may explain why more boys than girls have asthma, and after puberty, more women than men have asthma. Female sex hormones have an impact throughout the life span and are associated with poor asthma control. Gender may influence exposure to asthma triggers, and sex and gender can influence the prevalence of comorbidities and interactions with health care professionals. Despite widely reported sex- and gender-based differences in asthma and asthma management, these issues frequently are not considered by health care professionals. There is also inconsistency regarding the use of "sex" and "gender" in scientific discourse; research is needed to define sex- and gender-based differences better and how they might interact to influence asthma outcomes. This review outlines the impact an individual's sex and gender can have on the pathogenesis, clinical course, diagnosis, treatment, and management of asthma.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".