Neighborhood-Level Disadvantage Impacts on Patients with Fibrotic Interstitial Lung Disease
Bibliographic record
Abstract
Abstract Rationale Fibrotic interstitial lung disease (fILD) is a group of pathologic entities characterized by scarring of the lungs and high morbidity and mortality. Research investigating how socioeconomic and residential factors impact outcomes in patients with fILD is lacking. Objectives To determine the association between neighborhood-level disadvantage and presentation severity, disease progression, lung transplantation, and mortality in patients with fILD from the United States and Canada. Methods We performed a multicenter, international, prospective cohort study of 4,729 patients with fILD from one U.S. and eight Canadian ILD registry sites. Neighborhood-level disadvantage was measured by the area deprivation index in the United States and the Canadian Index of Multiple Deprivation in Canada. Measurements and Main Results In the U.S. but not in the Canadian cohort, patients with fILD living in neighborhoods with the greatest disadvantage (top quartile) experience the highest risk of mortality (hazard ratio = 1.51, P = 0.002), and in subgroups of patients with idiopathic pulmonary fibrosis, the top quartile of disadvantage experienced the lowest odds of lung transplantation (odds ratio = 0.46, P = 0.04). Greater disadvantage was associated with reduced baseline DL CO in both cohorts, but it was not associated with baseline FVC or FVC or DL CO decline in either cohort. Conclusions Patients with fILD who live in areas with greater neighborhood-level disadvantage in the United States experience higher mortality, and patients with idiopathic pulmonary fibrosis experience lower odds of lung transplantation. These disparities are not seen in Canadian patients, which may indicate differences in access to care between the United States and Canada.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".