The Clinical Frailty Scale for Risk Stratification in Patients With Fibrotic Interstitial Lung Disease
Bibliographic record
Abstract
BACKGROUND: Previous studies have shown the importance of frailty in patients with fibrotic interstitial lung disease (ILD). RESEARCH QUESTION: Is the Clinical Frailty Scale (CFS) a valid tool to improve risk stratification in patients with fibrotic ILD? STUDY DESIGN AND METHODS: Patients with fibrotic ILD were included from the prospective multicenter Canadian Registry for Pulmonary Fibrosis. The CFS was assessed using available information from initial ILD clinic visits. Patients were stratified into fit (CFS score 1-3), vulnerable (CFS score 4), and frail (CFS score 5-9) subgroups. Cox proportional hazards and logistic regression models with mixed effects were used to estimate time to death or lung transplantation. A derivation and validation cohort was used to establish prognostic performance. Trajectories of functional tests were compared using joint models. RESULTS: Of the 1,587 patients with fibrotic ILD, 858 (54%) were fit, 400 (25%) were vulnerable, and 329 (21%) were frail. Frailty was a risk factor for early mortality (hazard ratio, 5.58; 95% CI, 3.64-5.76, P < .001) in the entire cohort, in individual ILD diagnoses, and after adjustment for potential confounders. Adding frailty to established risk prediction parameters improved the prognostic performance in derivation and validation cohorts. Patients in the frail subgroup had larger annual declines in FVC % predicted than patients in the fit subgroup (-2.32; 95% CI, -3.39 to -1.17 vs -1.55; 95% CI, -2.04 to -1.15, respectively; P = .02). INTERPRETATION: The simple and practical CFS is associated with pulmonary and physical function decline in patients with fibrotic ILD and provides additional prognostic accuracy in clinical practice.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".