Evaluation of clinically relevant changes in the lung clearance index in children with cystic fibrosis and healthy controls
Bibliographic record
Abstract
BACKGROUND: The limits of reproducibility of the lung clearance index (LCI) are higher in children with cystic fibrosis (CF) compared with healthy children, and it is currently unclear what defines a clinically meaningful change. METHODS: % predicted and symptom scores at quarterly visits over 2 years. Two reviewers performed a detailed review of visits to evaluate the frequency that between visit LCI changes outside ±10%, ±15%, ±20% represented a clinically relevant signal. In the setting of acute respiratory symptoms, we used a generalised estimating equation model, with a logit link function to determine the ability of LCI worsening at different thresholds to predict failure of lung function recovery at follow-up. RESULTS: Clinically relevant LCI changes outside ±10%, ±15% and ±20% were observed at 25.7%, 15.0% and 8.3% of CF visits (n=744), respectively. The proportions of LCI changes categorised as noise, reflecting biological variability, were comparable between CF and HC at the 10% (CF 9.9% vs HC 13.0%), 15% (CF 4.3% vs HC 3.1%) and 20% (CF 2.4% vs HC 1.0%) thresholds. Compared with symptomatic CF visits without a worsening in LCI, events with ≥10% LCI increase were more likely to fail to recover baseline LCI at follow-up. CONCLUSION: The limits of reproducibility of the LCI in healthy children can be used to detect clinically relevant changes and thus inform clinical care in children with CF.
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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 | 0.017 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".