Performance Characteristics for Physiological Measures of Progressive Pulmonary Fibrosis
Bibliographic record
Abstract
Abstract Rationale Clinical measures of progressive pulmonary fibrosis (PPF) have been proposed, but their clinical utility remains unclear. Objectives To determine performance characteristics of lung function–based PPF measures, including new guideline criteria for discriminating clinically relevant outcomes. Methods A multicenter retrospective cohort analysis was performed to assess the performance characteristics of eight categorical measures of FVC and Dl CO decline, together with PPF guideline criteria (requiring two of the following: worsening respiratory symptoms, absolute decline in FVC ≥5% or Dl CO ≥15%, or radiological progression) for discriminating 2-year death or lung transplant among patients fibrotic interstitial lung disease from the United States, United Kingdom, and Canada (n = 2,727). The net benefit of the top-performing measures to inform treatment initiation were compared using decision curves. Measurements and Main Results PPF classified according to relative decline in FVC of ≥10%, relative decline in Dl CO of ≥15%, and PPF guideline criteria displayed the best overall test performance, with area under the receiver operating characteristic curves of 0.67–0.68. Specificity was higher than sensitivity for all evaluated measures, with relative measures of lung function decline outperforming absolute measures. The net benefit of standalone relative decline in FVC ≥10% and Dl CO ≥15% was similar to PPF guideline criteria across the range of treatment probability thresholds. Conclusions Classifying PPF by standalone measures of FVC and Dl CO decline provides clinical utility similar to PPF guideline criteria. Top-performing physiology-based measures of PPF discriminate outcomes with high specificity but low sensitivity.
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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 | 0.012 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".