Fat-Free Mass Index Controlled for Age and Sex and Malnutrition Are Predictors of Survival in Interstitial Lung Disease
Bibliographic record
Abstract
BACKGROUND: Literature focusing on nutritional variables and survival in interstitial lung disease (ILD) is limited by its focus on weight and BMI and has not considered body composition. OBJECTIVES: The primary objective of this study was to examine whether body composition measures, specifically fat-free mass index z-score (z-FFMI) and body fat mass index z-score (z-BFMI), were predictors of survival in fibrotic ILD patients. The second objective was to examine if nutrition status was a predictor of survival. METHOD: Seventy-eight outpatients diagnosed with fibrotic ILD were recruited in this cross-sectional study. Body composition data using dual frequency bioelectrical impedance analysis (BodyStat 1500MD; UK) and nutrition status using the subjective global assessment (SGA) were determined. To control for age and sex, z-FFMI and z-BFMI were calculated using population means. Participant charts were reviewed for diagnosis, age, disease severity, and exercise capacity. RESULTS: Age (HR 1.08, 95% CI [1.03-1.13], p < 0.01), BMI (HR 0.90, 95% CI [0.84-0.97], p < 0.01]), z-FFMI (HR 0.70, 95% CI [0.56-0.87], p = 0.02), z-BFMI (HR 0.74, 95% CI [0.57-0.96], p < 0.01), 6-min walk distance (6MWD) (HR 0.99, 95% CI [0.99-1.00], p < 0.01), percent predicted diffusing capacity for carbon monoxide (%DLco) (HR 0.93, 95% CI [0.89-0.97], p < 0.01), and severe malnutrition (SGA-C) (HR 6.98, 95% CI [2.00-24.27], p < 0.01) were significant predictors of survival. When controlled for exercise capacity and disease severity, z-FFMI and severe malnutrition were significant predictors of survival independent of %DLco. CONCLUSION: z-FFMI and severe malnutrition were significant predictors of survival in fibrotic ILD patients independent of disease severity.
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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 | 0.002 |
| 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) | 0.002 | 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".