Machine learning of spirometry may predict CLAD onset after lung transplantation
Bibliographic record
Abstract
Rationale: As Chronic Lung Allograft Dysfunction (CLAD) remains virtually unpredictable in clinical practice, we investigated the potential of routinely measured spirometry to predict CLAD onset, using machine learning. Methods: Of 1033 lung transplant recipients (transplanted 2010-2020), 383 random Controls without CLAD, and 186 CLAD patients (140 Bronchiolitis Obliterans/BOS - and 46 Restrictive Alllograft Syndrome/RAS) were included. Multiple measurements per patient were included using sliding windows, and spirometry parameters (FEV1, FEF25-75 and FVC; in Liters) were extracted from the patients’ electronic files. Time to CLAD was assessed as continuous response. Controls were censored at their last measurement. K-nearest neighbor (KNN) and random forest regression were compared, after which samples were stratified based on predicted time to CLAD onset. Results: KNN showed superior results compared to random forests, using 160 neighbors. Fig. 1a shows the fit between KNN-predicted and observed days to CLAD on our test set (R²=0.18). There were significant differences in CLAD-free survival between 5 stratified groups (Fig. 1b). Summary: Using machine learning on spirometry, lung transplant recipients may be stratified to risk groups with significant differences in subsequent time to CLAD onset. This allows to predict the risk of CLAD for each patient in real time.
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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 | 0.003 | 0.011 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".