P097 <break /> Interim Analysis of the EXCITING-ILD registry (Registry for Exploring Clinical and Epidemiological Characteristics of Interstitial Lung Diseases)
Bibliographic record
Abstract
Background: The epidemiologic knowledge on interstitial lung diseases (ILD) is limited. The multi-centre registry “EXCITING” collects data on characteristics, management and outcomes of all ILDs. Method: Since 10/2014 ILD patients are recruited prospectively. Results: Until 03/2016, 276 patients were included: 64% male, median age 64 years, 58% current/ex-smokers, symptoms >6 months before diagnosis: 51%. Medians: FVC 74%, DLCO 51%, GAP-ILD-Index 0-1 27%, 2-3 28%, 4-5 27%, 6-8 18%. Diagnostic procedures: 93% CT (47% HRCT), 73% BAL, 21% surgical-lung-biopsies; 57% multidisciplinary discussion. Patients had following diseases: IIP 45% (IPF 33%, NSIP 4%, DIP 2%, COP 3%, LIP 1%), hypersensitivity pneumonitis 14%, CTD-ILD 6%, pneumoconiosis 1%, sarcoidosis 21%, unclassifiable 6%, LAM 2%, drug-induced-ILD 3%, PAP 0.5%, eosinophilic pneumonia 0.5%, 2.9% familial forms. Relevant comorbidities: 24% GERD, 8% PH, 12% emphysema. Drug-therapies at baseline: Azathioprine 9%, Prednisolone 65%, NAC 12%, Pirfenidone 16%, Nintedanib 16%, Cyclophosphamide 4%, MTX 6%, MMF 2%, Rituximab 1%, ICS 14%, Sirolimus 1%, TNF-alpha-inhibitors 1%, clinical trials 5%. Further therapies: physiotherapy 3%, non-invasive ventilation 7%, long-term-oxygen 23%, inpatient pulmonary rehabilitation 5%; 0.4% listed for lung transplant. Hospitalisations within 6 months before inclusion: 52% (74% for ILD: pneumonia 12%, AE-ILD 19%, pneumothorax 2%). After 6 months follow-up: 25% hospitalisations (53% for ILD: 38% pneumonia), median relapse-free-survival (no decrease FVC ≥10%, or DLCO ≥15%, or death) is currently 24 months.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.006 |
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".