The risk stratification score: a new staging system for Idiopathic Pulmonary Fibrosis – discovery cohort results
Bibliographic record
Abstract
Introduction: Idiopathic pulmonary fibrosis (IPF) has a variable progression, requiring new staging systems for clinical decisions, including lung transplant (LTx). The RIsk Stratification scorE (RISE) is a 4-point index combining MRC dyspnea score (MRCDS), 6-minute walking distance (6MWD), and Composite Physiologic Index (CPI; CALIPER-revised) [1]. Methods: An ongoing 3-year observational, prospective, multicenter (London, CA, and Rome, IT) study investigates RISE’s prognostic performance in newly diagnosed IPF patients, including discovery and validation cohorts ( NCT02632123 ). We conducted a preliminary analysis on the discovery cohort with the primary endpoint of LTx-free survival. Results: Among 179 patients, 71 died, and 16 received a LTx (combined mortality/LTx 48.6%). Multivariate Cox regression analysis found that MRCDS ≥3, 6MWD ≤78% predicted, and CPI >23 independently predicted 3-year LTx-free survival. Receiver operating characteristic analysis indicated good diagnostic accuracy for these variables (area under the curve 0.775, 0.713, 0.868). Log-rank test showed strong predictive power for baseline and longitudinal changes in RISE (Chi square 67.04, 80.74). Conclusions: This analysis shows that RISE and its components predict LTx-free survival, validating it as a reliable staging system. Further confirmation is expected in the validation cohort. [1] Manzetti et al. BMC Pulm Med. 2021; 21:396 erj;66/suppl_69/PA5082/F1 F1 F1
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".