Racial, Ethnic, Sex, and Gender Equity for Inclusive Interstitial Lung Disease Research: An Official American Thoracic Society Research Statement
Bibliographic record
Abstract
Abstract Rationale Equitable care for all individuals living with interstitial lung disease (ILD) must be rooted in rigorous, high-quality research that is globally representative and inclusive. Objectives The objectives of this American Thoracic Society Statement are to emphasize the importance of racial, ethnic, sex, and gender considerations in ILD research; summarize existing evidence on differences and disparities in ILD care; and suggest practical recommendations to promote equitable inclusion in clinical research. Methods A multidisciplinary committee of experts in ILD, health equity, sex/gender, and race/ethnicity equity conducted a comprehensive review of the literature related to disparities in ILD. The group identified relevant studies pertaining to clinical trial participation and outcomes by race, ethnicity, sex, and gender. Consensus-based recommendations for equitable inclusion in research were developed through iterative discussion and agreement by all members. Main Results The review identified significant disparities across ILD domains, including epidemiology, diagnosis, management, treatment access, and clinical outcomes. Minority populations remain underrepresented in ILD research, especially in clinical trials of ILD treatment. Research efforts and programs in ILD must be based on inclusive practices. This can be accomplished by changing how subgroup data are collected, analyzed, and reported in ILD clinical trials, with greater attention to the inclusion of minority populations, at all levels of research. Conclusions Improving equity in ILD research is paramount to enhancing the generalizability and applicability of findings to the global ILD population. This goal will require coordinated action by all stakeholders, including researchers, institutions, funding agencies, and patient communities.
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 | 0.559 | 0.526 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.011 | 0.022 |
| Scholarly communication | 0.019 | 0.020 |
| Open science | 0.009 | 0.020 |
| Research integrity | 0.024 | 0.036 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".