Epidemiology and survival of idiopathic pulmonary fibrosis from national data in Canada
Bibliographic record
Abstract
Idiopathic pulmonary fibrosis (IPF) is a rare disease, with estimates of prevalence varying considerably across countries due to paucity in data collection. The aim of this study was to investigate the prevalence and incidence of IPF in Canada using administrative data requiring minimal extrapolation.We used mandatory national administrative data from 2007-2011 to identify IPF cases of all ages with an International Classification of Diseases (Version 10, Canadian) diagnosis code of J84.1. We used a broad definition that excluded cases with subsequent diagnosis of other interstitial lung diseases, and a narrow definition that required further diagnostic testing prior to IPF diagnosis. We explored survival and quality of life.For all ages, the broad prevalence of IPF was 41.8 per 100 000 (14 259 cases) and was higher for men. The incidence rate was 18.7 per 100 000 (6390 cases) and was higher for men. The narrow prevalence was 20.0 per 100 000 (6822 cases) and incidence was 9.0 per 100 000 (3057 cases). The 4-year risk of death was 41.0% and the quality of life with IPF after 2 years was lower than for Global Initiative for Chronic Obstructive Lung Disease stage IV chronic obstructive pulmonary disease.Using comprehensive national data, the prevalence of IPF in Canada was higher than other national estimates, suggesting that either IPF may be more common in Canada or that data capture may have been previously limited.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".