Sex and gender in lung health and disease: more than just Xs and Ys
Bibliographic record
Abstract
Every nucleated human cell carries sex chromosomes and every person has a gender. These terms “sex” and “gender” are often used interchangeably and incorrectly in the scientific literature, but they mean different things: sex is a biological attribute associated with physical and physiological features determined by chromosomes, hormones, anatomy and gene expression; whereas gender is a sociocultural construct based on expressions of identity, behaviours and social roles [1]. Thus, sex and gender-related factors can both influence health and disease via distinct but intersecting mechanisms. There are important sex differences in lung development that start in utero and during childhood, which ultimately influence respiratory function in adulthood. Women are predisposed to higher rates of several pulmonary diseases such as asthma and pulmonary arterial hypertension. Gender-related factors can also influence pulmonary disease in a variety of ways, including gender differences in occupational exposures, health-seeking behaviours or access to healthcare, and other behaviours such as tobacco use. A new series explores the role of sex and gender-related factors in respiratory physiology, lung health, and across respiratory diseases
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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.021 | 0.025 |
| Insufficient payload (model declined to judge) | 0.011 | 0.009 |
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".