Addressing sex and gender to improve asthma management
Bibliographic record
Abstract
Sex (whether one is 'male' or 'female', based on biological characteristics) and gender (defined by socially constructed roles and behaviors) influence asthma diagnosis and management. For example, women generally report more severe asthma symptoms than men; men and women are exposed to different asthma-causing triggers; men tend to be more physically active than women. Furthermore, implicit, often unintended gender bias by healthcare professionals (HCPs) is widespread, and may result in delayed asthma diagnosis, which can be greater in women than men. The sex and gender of the HCP can also impact asthma management. Pregnancy, menstruation, and menopause can all affect asthma in several ways and may be associated with poor asthma control. This review provides guidance for considering sex- and gender-associated impacts on asthma diagnosis and management and offers possible approaches to support HCPs in providing personalized asthma care for all patients, regardless of their sex or gender.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".