Lung Clearance Index to Track Acute Respiratory Events in School-Age Children with Cystic Fibrosis
Bibliographic record
Abstract
Abstract Rationale The lung clearance index (LCI) is responsive to acute respiratory events in preschool children with cystic fibrosis (CF), but its utility to identify and manage these events in school-age children with CF is not well defined. Objectives To describe changes in LCI with acute respiratory events in school-age children with CF. Methods In a multisite prospective observational study, the LCI and FEV1 were measured quarterly and during acute respiratory events. Linear regression was used to compare relative changes in LCI and FEV1% predicted at acute respiratory events. Logistic regression was used to compare the odds of a significant worsening in LCI and FEV1% predicted at acute respiratory events. Generalized estimating equation models were used to account for repeated events in the same subject. Measurements and Main Results A total of 98 children with CF were followed for 2 years. There were 265 acute respiratory events. Relative to a stable baseline measure, LCI (+8.9%; 95% confidence interval, 6.5 to 11.3) and FEV1% predicted (−6.6%; 95% confidence interval, −8.3 to −5.0) worsened with acute respiratory events. A greater proportion of events had a worsening in LCI compared with a decline in FEV1% predicted (41.7% vs. 30.0%; P = 0.012); 53.9% of events were associated with worsening in LCI or FEV1. Neither LCI nor FEV1 recovered to baseline values at the next follow-up visit. Conclusions In school-age children with CF, the LCI is a sensitive measure to assess lung function worsening with acute respiratory events and incomplete recovery at follow-up. In combination, the LCI and FEV1 capture a higher proportion of events with functional impairment.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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".