Lung clearance index in cystic fibrosis subjects treated for pulmonary exacerbations
Bibliographic record
Abstract
Pulmonary exacerbations are important clinical events for cystic fibrosis (CF) patients. Studies assessing the ability of the lung clearance index (LCI) to detect treatment response for pulmonary exacerbations have yielded heterogeneous results. Here, we conduct a retrospective analysis of pooled LCI data to assess treatment with intravenous antibiotics for pulmonary exacerbations and to understand factors explaining the heterogeneous response.A systematic literature search was performed to identify prospective observational studies. Factors predicting the relative change in LCI and spirometry were evaluated while adjusting for within-study clustering.Six previously reported studies and one unpublished study, which included 176 pulmonary exacerbations in both paediatric and adult patients, were included. Overall, LCI significantly decreased by 0.40 units (95% CI -0.60- -0.19, p=0.004) or 2.5% following treatment. The relative change in LCI was significantly correlated with the relative change in forced expiratory volume in 1 s (FEV1), but results were discordant in 42.5% of subjects (80 out of 188). Higher (worse) baseline LCI was associated with a greater improvement in LCI (slope: -0.9%, 95% CI -1.0- -0.4%).LCI response to therapy for pulmonary exacerbations is heterogeneous in CF patients; the overall effect size is small and results are often discordant with FEV1.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| 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".