The Canadian Registry for Pulmonary Fibrosis: Design and Rationale of a National Pulmonary Fibrosis Registry
Bibliographic record
Abstract
Background. The relative rarity and diversity of fibrotic interstitial lung disease (ILD) have made it challenging to study these diseases in single-centre cohorts. Here we describe formation of a multicentre Canadian registry that is needed to describe the outcomes of fibrotic ILD and to enable detailed healthcare utilization analyses that will be the cornerstone for future healthcare planning. Methods. The Canadian Registry for Pulmonary Fibrosis (CARE-PF) is a prospective cohort anticipated to consist of at least 2,800 patients with fibrotic ILD. CARE-PF will be used to (1) describe the natural history of fibrotic ILD, specifically determining the incidence and outcomes of acute exacerbations of ILD subtypes and (2) determine the impact of ILD and acute exacerbations of ILD on health services use and healthcare costs in the Canadian population. Consecutive patients with fibrotic ILD will be recruited from five Canadian ILD centres over a period of five years. Patients will be followed up as clinically indicated and will complete standardized questionnaires at each clinic visit. Prespecified outcomes and health services use will be measured based on self-report and linkage to provincial health administrative databases. Conclusion. CARE-PF will be among the largest prospective multicentre ILD registries in the world, providing detailed data on the natural history of fibrotic ILD and the healthcare resources used by these patients. As the largest and most comprehensive cohort of Canadian ILD patients, CARE-PF establishes a network for future clinical research and early phase clinical trials and provides a platform for translational and basic science research.
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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 | 0.113 | 0.089 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".