The lung clearance index as a monitoring tool in cystic fibrosis
Bibliographic record
Abstract
PURPOSE OF REVIEW: In cystic fibrosis, (CF) there is an urgent need for objective tests that can capture and track preclinical lung disease. The lung clearance index (LCI), the primary outcome measure of the multiple breath washout test, is an established endpoint in clinical trials but the clinical utility of the test remains poorly defined. The purpose of this review is to examine the key studies over the past years that have advanced our understanding of the role of the LCI in clinical practice. RECENT FINDINGS: The variability of LCI measurements increases with lung disease severity, and new evidence shows that between-visit changes in the LCI are therefore best expressed as a relative rather than an absolute change. A relative change of greater than 15% between visits is likely outside the intrinsic variability of the test and physiologically relevant. The LCI is feasible to perform and is a more sensitive outcome measure than forced expiratory volume in one second (FEV1). The LCI correlates with outcome measures such as structural MRI, and shows great promise in the routine clinical monitoring of CF lung disease, particularly in younger patients with milder disease. SUMMARY: Recent studies have progressed our understanding of the role of the LCI in clinical practice, but the exact clinical utility of the test in monitoring CF lung disease is still uncertain.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".