Genomic Testing of Patients With Interstitial Lung Disease: Current International Practice
Bibliographic record
Abstract
Abstract Rationale: Gene-environment interactions play a significant role in the pathogenesis of interstitial lung disease (ILD), with 20-30% of ILD risk attributed to genetic predisposition. However, genomic testing is not routinely integrated into clinical practice or guidelines. This study aimed to explore current practices in ILD genomic testing on a global scale to identify barriers and inform strategies for broader implementation. Methods: Semi-structured interviews were conducted with ILD experts to understand current clinical practice for genomic testing of ILD patients and predisposed relatives. Experts were selected based on their publication history or clinical experience with ILD Genetics, ensuring wide international representation. Only one expert per center was interviewed to avoid duplication of responses. Interviews followed an open, semi-structured format using broad predefined topics including training and clinic infrastructure, familial ILD case finding, genomic tests and methods, impact on care (including relatives), research, and governance. Interview transcripts were analyzed using deductive qualitative methods. Results: Seventeen interviews were conducted, with a median duration of 39 minutes (range 32 – 61). A wide variety of approaches to genomic testing in ILD practice were identified. Four key themes emerged (Table 1 ): a) Setting of testing: The need for genetic counselling, documented informed consent and appropriate training of ILD clinicians to interpret the results, given the impact on ILD patients and their relatives; b) Impact on clinical care: The influence of genomic testing on clinical decisions, and support by multidisciplinary teams; c) Specific tests: Heterogeneity in the choice of tests (e.g. telomere length measurement, gene panels, sequencing) with the sequence of testing, sample types, and laboratory methods often determined by local availability; d) Data safety: Concerns were raised on governance and data handling, with several experts highlighting gaps in how genomic data is shared and regulated. Conclusions: There is global heterogeneity in ILD genomic testing. The themes identified in this study will help inform a framework to improve access to testing. The next steps are to conduct an international mapping of access to ILD genomic testing and a Delphi survey for consensus on the core features needed for familial ILD services. This will help to raise awareness, increase worldwide collaboration and to support the development of clinical pathways.
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 | 0.027 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 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".