Use of 6-min walk distance to predict lung transplant-free survival in fibrosing non-IPF interstitial lung diseases
Bibliographic record
Abstract
Introduction: Detecting disease progression in patients with fibrosing non-IPF interstitial lung diseases (ILDs) is challenging. We hypothesize that changes in 6-min walk distance (6MWD) from baseline, along with lung function measures, can predict survival and indicate clinically significant progression. Methods: Forced vital capacity (FVC), diffusing lung capacity (DLCO) and 6MWD were considered both at baseline and at 1 year in a discovery cohort (n=105) and in a validation cohort (n=138) from different centers. The primary endpoint was lung transplant (LTx)-free survival. Results: Average follow-up was 3 years in both cohorts. Combined incidence of deaths and LTx was 29% and 21%, respectively. No collinearity and no strong correlations were observed among FVC, DLCO and 6MWD longitudinal changes. While age, gender and BMI were not significant, 6MWD decline ≥24 m predicted LTx-free-survival significantly and independently from FVC and DLCO declines, with high sensitivity and specificity, in both the discovery and the validation cohorts. Although FVC and DLCO declines remained significant predictors of LTx-free survival, 6MWD decline was more accurate than the proposed ATS/ERS/JRS/ALAT functional criteria. Results were confirmed after stratifying patients by baseline FVC. Discussion: Longitudinal declines of 6MWD are associated with poor survival in fibrosing ILDs across a wide range of baseline severity, with high accuracy. 6MWD longitudinal decline is largely independent from lung function decline and may be integrated into the routine assessment of progression.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".