An international survey on genetics in clinical practice for interstitial lung disease
Bibliographic record
Abstract
Background: Recent advances in medical science have increased our understanding of the role of genetic factors in interstitial lung disease (ILDs), especially in some types of pulmonary fibrosis, but the impact of genetics on clinical practice is unknown. Aims and objectives: We developed a parallel online survey for pulmonologists and ILD patients and their relatives to evaluate current practice, perceptions, and needs regarding genetics in ILD. It was translated in seven languages for patients and relatives. Methods: The survey was built in the secure web application REDCap, and distributed via several patient and physician organizations. The main topics of the survey for pulmonologists were: indication for genetic analysis, informing patients and their relatives, clinical impact, screening relatives, and healthcare impact. For patients and relatives, we included: diagnosis and treatment, familial disease, genetic tests, information, and screening. Several questions were included in both surveys to assess whether pulmonologists and patients and their relatives have overlapping perceptions. Results: The surveys close on 1 March 2021. By now, 295 pulmonologists from 59 countries, and 455 patients and 177 relatives from 21 different countries worldwide responded. Main diagnoses of patients include pulmonary fibrosis and sarcoidosis. Conclusions: The high response from both patients and pulmonologist will yield valuable data. It is anticipated that the surveys provide actionable results to improve patient care.
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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 | 0.005 | 0.014 |
| 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) | 0.003 | 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".