Percent Predicted vs. Absolute Six-Minute Walk Distance as Predictors of Lung Transplant-Free Survival in Fibrosing Interstitial Lung Diseases
Bibliographic record
Abstract
INTRODUCTION: Fibrosing interstitial lung diseases (ILDs) often progress despite treatment and become life-threatening, with lung transplant (LTx) remaining the only curative option. Six-minute walk distance (6MWD) is increasingly recognized as reliable predictor of clinical course, especially when longitudinally considered. The use of reference equations to express 6MWD as percent predicted (6MWD%) has not been previously studied in fibrosing ILDs. We sought to investigate whether the prognostic power of 6MWD% is superior to that of 6MWD expressed in meters (6MWD-m). METHODS: A retrospective, multicenter cohort analysis was conducted on both idiopathic pulmonary (IPF) and non-IPF fibrosing ILD patients. Patients were divided into a discovery (n = 211) and a validation (n = 260) cohort. Longitudinal changes of 6MWD% and lung function parameters were simultaneously considered. LTx-free survival at 3 years from baseline was the endpoint. Competing risks of death and LTx were considered. RESULTS: Baseline 6MWD% and its longitudinal changes were significant predictors of LTx-free survival and independent from lung function variables. In both cohorts, on multivariate cox proportional hazard regression analysis, receiver operating characteristics analysis and Kaplan-Meier estimates, 6MWD% was consistently, but only slightly superior to 6MWD-m as a predictor of LTx-free survival. CONCLUSION: 6MWD% has only a slight, yet detectable advantage over 6MWD-m as a predictor of survival in fibrosing ILDs. Utilizing 6MWD% may aid in risk stratification, treatment monitoring, and LTx timing optimization. However, available reference equations do have predicting limitations. Refined predictive equations and standardizing reporting practices are therefore needed to further enhance the clinical utility of 6MWD% in fibrosing ILDs.
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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 | 0.002 | 0.004 |
| 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".