Sex and gender in interstitial lung diseases
Bibliographic record
Abstract
Sex and gender differences influence key domains of research, lung health, healthcare access and healthcare delivery. In interstitial lung diseases (ILDs), mouse models of pulmonary fibrosis are clearly influenced by sex hormones. Additionally, short telomeres, a biomarker of telomere regulation gene mutations, are impacted by sex, while heritability unexplained by genetic variation may be attributable to gendered environmental factors that drive epigenetic control. Diseases like idiopathic pulmonary fibrosis, hypersensitivity pneumonitis, occupational ILDs, connective tissue-associated ILDs and lymphangioleiomyomatosis have different prevalence and prognosis between men and women. These differences arise from a complex interplay between biological sex and sociocultural gender influencing genetics, epigenomic modifiers, hormones, immune function, response to treatment and interaction with healthcare systems. Much work remains to be done to systematically integrate sex and gender analysis into relevant domains of science and clinical care in ILD, from strategic considerations for establishing research priorities to guidelines for establishing best clinical practices. Accounting for sex and gender in ILD is essential to the practice of individualised, patient-centred medicine.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".