Frailty is an independent predictor of number and length of hospitalizationsin patients with fibrotic ILD
Bibliographic record
Abstract
Objective: To determine the impact of frailty on risk of non-elective hospitalizations and length of stay (LOS) in patients with fibrotic interstitial lung disease (ILD). Methods: Frailty was measured as the proportion of deficits present on a 42-item patient-reported frailty index (FI), with frailty defined by a FI>0.21. Mixed effects poisson regression was used to estimate incidence rate ratios (IRRs) for number of non-elective hospitalizations within 6 months of frailty assessment and Cox proportional hazard ratios (HRs) to analyze time to discharge. Results: 540 patients with fibrotic ILD were recruited, including 100 with idiopathic pulmonary fibrosis (IPF). Median FI was 0.21 (IQR 0.09-0.33) with 50% of patients classified as frail. A total of 214 hospitalizations were recorded, including 131 that were respiratory-related. IRRs for all-cause and respiratory-related hospitalizations were both 1.03 (95%CI 1.02-1.04, p<0.001) for every 0.01 increase in FI; frailty remained a significant predictor with adjustment for age, sex, IPF diagnosis, FVC%, and DLCO%. Frail patients had 2.3 times the rate of all-cause (95%CI 1.6-3.3, p<0.001) and IRR 2.0 (95%CI 1.3-3.2, p<0.001) for respiratory-related hospitalizations. Median LOS was 6 days (IQR 2.8-16.5) in frail and 3 days (IQR 2-10) in non-frail patients, adjusted HR 1.02 (95%CI 1.01-1.03, p=0.009) per 0.01 FI (Figure). Conclusion: Frailty independently predicts number and length of all-cause and respiratory-related hospitalizations in patients with fibrotic ILD.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".