Genetic testing in interstitial lung disease: An international survey
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Genetic analysis is emerging for interstitial lung diseases (ILDs); however, ILD practices are not yet standardized. We surveyed patients', relatives' and pulmonologists' experiences and needs on genetic testing in ILD to evaluate the current situation and identify future needs. METHODS: A clinical epidemiologist (MT) together with members of the ERS taskforce and representatives of the European Idiopathic Pulmonary Fibrosis and related disorders Federation (EU-IPFF) patient organisation developed a survey for patients, relatives and pulmonologists. Online surveys consisted of questions on five main topics: awareness of hereditary ILD, the provision of information, genetic testing, screening of asymptomatic relatives and clinical impact of genetic analysis in ILD. RESULTS: Survey respondents consisted of 458 patients with ILD, 181 patients' relatives and 352 pulmonologists. Most respondents think genetic testing can be useful, particularly for explaining the cause of disease, predicting its course, determining risk for developing disease and the need to test relatives. Informing patients and relatives on genetic analysis is primarily performed by the pulmonologist, but 88% (218) of pulmonologists identify a need for more information and 96% (240) ask for guidelines on genetic testing in ILD. A third of the pulmonologists who would offer genetic testing currently do not offer a genetic test, primarily because they have limited access to genetic tests. Following genetic testing, 72% (171) of pulmonologists may change the diagnostic work-up and 57% (137) may change the therapeutic approach. CONCLUSION: This survey shows that there is wide support for implementation of genetic testing in ILD and a high need for information, guidelines and access to testing among patients, their relatives and pulmonologists.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".