P1 Global overview of incidence and prevalence of interstitial lung disease: a systematic literature review
Bibliographic record
Abstract
Introduction Interstitial lung diseases (ILD) are a diverse group of pulmonary fibrotic and inflammatory conditions. The global burden of ILD is largely unknown, in part because of differences between countries in diagnosis and in coding practices1. Reviews to date have therefore tended to be limited in geographical scope and focused on individual ILDs. Our aim was to systematically review evidence for ILD prevalence and incidence on a global scale. Methods A systematic search of Medline and Embase was conducted to identify relevant articles reporting the incidence and/or prevalence of individual ILD subtypes. The search was limited to observational studies published between 2015 and 2020 in English, with articles independently screened by two reviewers. An adapted Newcastle-Ottawa scale was used to assess quality and risk of bias. Results Of 8,560 articles, 51 studies were included. Geographically, most studies were from Asia (47.1%) and Europe (43.1%). Significant heterogeneity was noted in the incidence and prevalence (figure 1) of ILDs between different countries. These variations are largely attributed to the diversity of the underlying population and differences in data sources used. For example, the prevalence of systemic sclerosis ILD ranged from 30% in Europe to 71% in Asia, silicosis ranged from 0.02% in the USA to 37% in Brazil. IPF was the most reported ILD, the range of prevalence was 8.5 to 38.8 per 100,000 persons, and incidence was 2.4 to 48.5 per 100,000 persons-years, across regions. Discussion There have been few reviews investigating the epidemiology of ILDs overall or by subtypes. ILD publications differed by region, for example, more studies from Asia explored occupational ILDs, whereas more European studies reported on autoimmune ILDs. With an increasing research interest in progressive fibrosing ILDs, there is a need to understand the global burden of ILD and highlight unmet needs. Reference King TE. Am J Respir Crit Care Med. 2005;172:268–279.
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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 | 0.011 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.037 | 0.033 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".