Diagnosis, screening, and follow-up of patients with familial interstitial lung disease: Results from an international survey
Bibliographic record
Abstract
BACKGROUND: Advances in the field of genetics of interstitial lung diseases (ILDs) have led to the recent consensus statements made by expert groups. International standards for genetic testing in ILD have not yet been established. We aimed to examine current real-world strategies employed by pulmonologists working with familial ILD. METHODS: A panel of pulmonologists with expertise in ILD developed an international survey aimed at clinicians working with ILD. The survey consisted of 74 questions divided into eight topics: characteristics of respondents, diagnosis, screening of first-degree relatives, screening tools, genetic testing methods, lung transplantation, ethical concerns, and future needs. RESULTS: Overall, 237 pulmonologists from 50 countries participated. A family history of ILD was asked for by 91% of respondents while fewer asked for symptoms related to telomere disorders. Respondents stated that 59% had access to genetic testing, and 30% to a genetic multidisciplinary team (MDT). Many respondents were unaware of specific genetic testing methods. Pathogenic genetic variants were seen as a potential contraindication for lung transplantation in 6-8% of respondents. Genetic screening of relatives was supported by 80% of respondents who indicated insufficient evidence and a lack of formal guidelines for genetics and ILD. Only 16% had a standardized program. CONCLUSION: Most pulmonologists ask for a family history of ILD and recommend genetic testing for ILD and screening in relatives but have limited knowledge of specific tests and access to genetic MDT. Evidence-based guidelines to inform patients, relatives, and physicians are still warranted.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".