Relative versus absolute change in forced vital capacity in idiopathic pulmonary fibrosis
Bibliographic record
Abstract
BACKGROUND: Decline in forced vital capacity (FVC) over time reliably predicts mortality in patients with idiopathic pulmonary fibrosis. The use of this measure in clinical practice is recommended by current evidence-based guidelines. It is unknown if the method of calculating decline in FVC (relative vs. absolute change) impacts its frequency or its ability to predict mortality. METHODS: Patients with idiopathic pulmonary fibrosis from two prospective cohorts were included if they had a baseline and 12-month follow-up FVC. A ≥10% decline in FVC from baseline was calculated in two ways: a relative decline of 10% (e.g., from 60% predicted to 54% predicted) and an absolute decline of 10% (e.g., from 60% predicted to 50% predicted). The frequency of a ≥10% decline in FVC and its ability to predict 2-year transplant-free survival were compared between these two methods. Declines in FVC of ≥5% and ≥15% were similarly compared. Analyses were performed unadjusted and adjusted for age, gender, use of oxygen, baseline FVC and baseline diffusion capacity for carbon monoxide. RESULTS: The frequency of any given FVC decline was significantly greater using the relative change in FVC method. For ≥10% decline, both methods predicted 2-year transplant-free survival with similar accuracy, and remained significant predictors after adjusting for baseline characteristics. The absolute change method appeared more predictive for ≥5% decline. CONCLUSIONS: Using the relative change in FVC maximises the chance of identifying a ≥10% decline in FVC without sacrificing prognostic accuracy. This may not hold true for ≥5% decline in FVC. These findings have important implications for clinical practice and the design of clinical trials.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".