Impact of surgical lung biopsy on lung function and survival in patients with idiopathic pulmonary fibrosis in a multi‐centre registry cohort
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Establishing an accurate and timely diagnosis of idiopathic pulmonary fibrosis (IPF) is essential for appropriate management and prognostication. In some cases, surgical lung biopsy (SLB) is performed but carries non-negligible risk. The objective of this retrospective study was to determine if SLB is associated with accelerated lung function decline in patients with IPF using the Canadian Registry for Pulmonary Fibrosis. METHODS: Linear mixed models and Cox proportional hazards regression models were used to compare decline in forced vital capacity (FVC)%, diffusion capacity of the lung (DLCO%) and risk of death or lung transplantation between SLB and non-SLB patients. Adjustments were made for baseline age, sex, smoking history, antifibrotic use, and lung function. A similar analysis compared lung function changes 12 months pre- and post-SLB. RESULTS: A total of 81 SLB patients and 468 non-SLB patients were included. In the SLB group, the post-biopsy annual FVC% decline was 2.0% (±0.8) in unadjusted, and 2.1% (±0.8) in adjusted models. There was no difference in FVC% decline, DLCO% decline, or time to death or lung transplantation between the two groups, in adjusted or unadjusted models (all p-values >0.07). In the pre-post SLB group, no differences were identified in FVC% decline in unadjusted or adjusted models (p = 0.07 for both). CONCLUSION: No association between SLB and lung function decline or risk of death or lung transplantation was identified in this multi-centre study of patients with 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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".