Mortality and survival in idiopathic pulmonary fibrosis: a systematic review and meta-analysis
Bibliographic record
Abstract
Background There are substantial advances in diagnosis and treatment for idiopathic pulmonary fibrosis (IPF), but without much evidence available on recent mortality and survival trends. Methods A narrative synthesis approach was used to investigate the mortality trends, then meta-analyses for survival trends were carried out based on various time periods. Results Six studies reported the mortality data for IPF in 22 countries, and 62 studies (covering 63 307 patients from 20 countries) reported survival data for IPF. Age-standardised mortality for IPF varied from ∼0.5 to ∼12 per 100 000 population per year after year 2000. There were increased mortality trends for IPF in Australia, Brazil, Belgium, Canada, Czech Republic, Finland, France, Germany, Hungary, Italy, Lithuania, the Netherlands, Poland, Portugal, Spain, Sweden and UK, while Austria, Croatia, Denmark, Romania and the USA showed decreased mortality trends. The overall 3-year and 5-year cumulative survival rates (CSRs) were 61.8% (95% CI 58.7–64.9; I 2 =97.1%) and 45.6% (95% CI 41.5–49.7; I 2 =97.7%), respectively. Prior to 2010, the pooled 3-year CSR was 59.9% (95% CI 55.8–64.1; I 2 =95.8%), then not significantly (p=0.067) increased to 66.2% (95% CI 62.9–69.5; I 2 =92.6%) in the 2010s decade. After excluding three studies in which no patients received antifibrotics after year 2010, the pooled 3-year CSRs significantly (p=0.039) increased to 67.4% (95% CI 63.9–70.9; I 2 =93.1%) in the 2010s decade. Discussion IPF is a diagnosis associated with high mortality. There was no observed increasing survival trend for patients with IPF before year 2010, with then a switch to an improvement, which is probably multifactorial.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.032 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".