P017 <break /> Clinical Characteristics of Interstitial Lung Disease Patients: Report from a Single Center Longitudinal Database
Bibliographic record
Abstract
Background: The interstitial lung diseases (ILDs) are a large group of disorders with heterogeneous clinical presentations and outcomes. The University of Calgary longitudinal ILD database was established to characterize the clinical characteristics and outcomes of ILD patients from a single center. Methods: Consecutive patients were prospectively enrolled in the longitudinal database between 2007 and 2013, with data collected up to May 2016. Baseline demographic features and autoimmune serology plus longitudinal lung function, 6-minute walk test and survival data were collected over follow-up time. Descriptive statistics were used to characterize the cohort. Results: Of 198 patients enrolled, 187 with complete records were included in this analysis with a mean follow-up duration of 4.7 (±2.6) years (Table). The mean age was 65 (±12.6) years with 56% of the cohort being female. Mean baseline forced vital capacity was 78% predicted (±20.9) and mean diffusion capacity of the lung for carbon monoxide was 60.3% predicted (±21.2). Eighty-one patients (43.3%) died and 5 (2.7%) underwent lung transplantation. Of the 187 patients, 79 (42.3%) had idiopathic pulmonary fibrosis, 58 (31.0%) had connective tissue-associated ILD, and 48 (25.67%) were unclassifiable. Of these, the differential diagnosis for 31 patients (65%) included idiopathic non-specific interstitial pneumonia vs. IPF.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".