Frailty is common and strongly associated with dyspnoea severity in fibrotic interstitial lung disease
Bibliographic record
Abstract
ABSTRACT Background and objective Frailty is the age‐related accumulation of deficits that decrease the ability to respond to biological stress. Patients with fibrotic interstitial lung disease ( ILD ) may be frail due to consequences of ILD , age, co‐morbidities and adverse effects of pharmacotherapies. The objective of this study was to examine the prevalence and predictors of frailty in fibrotic ILD . Methods Fibrotic ILD patients were recruited from a specialized clinic. Patients with ILD secondary to a systemic disease were excluded. Frailty was determined using the Frailty Index based on the presence or absence of multiple deficits, including co‐morbidities, symptoms and functional limitations. The Frailty Index was based on the proportion of deficits present, with frailty defined as a score >0.21. Cronbach's alpha was used to estimate the internal consistency of the Frailty Index. Dyspnoea was measured using the University of California San Diego Shortness of Breath Questionnaire. Multivariate analysis was used to determine independent predictors of frailty. Results The definition of frailty was met in 50% of the 129 patients. Cronbach's alpha for the Frailty Index was 0.87. The Frailty Index was associated with forced vital capacity ( FVC ), forced expiratory volume in 1 s ( FEV 1 ), diffusion capacity of the lung for carbon monoxide ( DL CO ), ILD ‐gender, age and physiology ( GAP ) index, composite physiologic index and dyspnoea score. Dyspnoea severity was the strongest unadjusted predictor (r = 0.65, P < 0.001) and only independent predictor of the Frailty Index (0.034 increase in Frailty Index per 10‐point increase in dyspnoea score; R 2 = 0.37; P < 0.001). Conclusion Frailty is highly prevalent and is strongly and independently associated with dyspnoea severity, demonstrating that dyspnoea is a more important determinant of frailty than pulmonary function.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".