Bibliographic record
Abstract
Differences in disease susceptibility and prognosis between men and women are known to occur in the incidence and development of neurodegenerative, cardiovascular, and immunological disorders. In the lung there are also sex-based differences in the incidence, prevalence, and pathogenesis of lung cancer, cystic fibrosis, COPD, and asthma. In the general population, sex-based differences in asthma have been shown by epidemiologic studies, but unfortunately these studies are not consistent in their conclusions. This variability in human epidemiological studies justifies the need for more focused studies of the effects of specific hormones. Such specific mechanistic studies can most easily be performed in animal models, and since mouse models have the potential for separating specific genetic factors from environmental and exogenous factors, this species has become increasingly important in the design, analysis, and interpretation of asthma research. This review will document the male and female differences in airway function of naïve and sensitized mouse models, as well as the great variability in the functional measurements of airway tone. Until the situation is better understood, this variability between males and females should be kept in mind when designing, analyzing, and interpreting studies of smooth muscle responses in animal models and human subjects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".