Baseline Characteristics and Survival Trends for Patients Diagnosed With Idiopathic Pulmonary Fibrosis Over 2016 – 2022
Bibliographic record
Abstract
Abstract Background: Idiopathic pulmonary fibrosis (IPF) is historically quoted as having a median survival of 2-5 years, but this is largely based on older data prior to the introduction of antifibrotic drugs. We therefore aimed to identify recent trends in both patient characteristics and survival. Methods: We conducted a secondary analysis of the prospective Canadian Registry for Pulmonary Fibrosis (CARE-PF). Incident cases of IPF between 2016-2022 with a baseline pulmonary function test within 6 months of diagnosis were included. Mean age, forced vital capacity (FVC), and diffusing capacity of the lung for carbon monoxide (DLCO) were calculated according to year of IPF diagnosis. Transplant-free survival was determined based on the time from IPF diagnosis. The 3-year survival probability of patients diagnosed in each calendar year was determined using Kaplan-Meier estimates. Monotonic trends over time were evaluated using the Mann-Kendall test. Results: The cohort included 760 patients with IPF. Mean age at diagnosis was 71 years, 75% were male, and 77% were ever-smokers with a median smoking history of 25 pack-years. The mean age at IPF diagnosis was 69 years in 2016 and rose somewhat to 72 by 2022 (p=0.003) (Figure 1A). Over the same period, the mean baseline FVC increased from 79% (95%CI 76-83%) to 85% (95%CI 80-90%) (p=0.02), while baseline DLCO did not show a trend, remaining within a range of 53-61% (p=0.37) (Figure 1B). The 3-year survival probability started at 74% (95%CI 67-82%) in 2016 and increased to 84% (95%CI 76-92%) in 2021 (Figure 1C), but this was not associated with a statistically significant upward trend (p=0.06). Conclusion: Both age and baseline FVC at time of IPF diagnosis rose over 2016-2022. The 3-year survival in IPF remained stable over 2016-2022 and was generally >70%, which is higher than previously reported. This may reflect both earlier disease detection (lead time bias), increasing uptake of antifibrotic therapy over time, and improved overall care. Figure 1
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".