Impact of the Lung Clearance Index on clinical decision-making in children with cystic fibrosis
Bibliographic record
Abstract
Background: The Lung Clearance Index (LCI) is a key outcome measure in paediatric cystic fibrosis (CF) clinical trials. While LCI is used clinically in select European centres, evidence of its utility in informing clinical decisions is an important factor in deciding whether LCI will be adopted more widely. Methods: Clinical vignettes were created to assess how providers manage pulmonary exacerbations and adjust inhaled therapies. Two vignettes acted as controls, whereby LCI was not expected to change treatment decisions. Conducted via REDCap®, providers initially used standard clinical data to make treatment decisions, then the LCI result was revealed and they were asked to incorporate LCI into their decision making. Results: Overall, 62 providers completed 522 vignettes. Of the 49 providers that completed all 10 vignettes and the demographic questions, 31 were Canadian and 18 were from the US. The majority of providers were CF physicians (37) followed by nurses (5) nurse practitioners (4) and others (3). Overall, LCI changed clinical decisions in 18.4% (95% CI 15.3 - 21.9%), supported decisions in 57.1% (52.7 - 61.3%), and had no impact in 24.5% (21.0 - 28.4%). Of the 104 responses to the control vignettes, LCI changed the decision in 7.7% (3.8 - 14.8%), supported the decision in 77.9% (68.7 - 84.9%) and had no impact in 14.4% (8.8 - 22.7%). Conclusion: This simulated study shows that LCI significantly influences treatment decisions in children with CF. These results will inform the design and sample size of a real-world study to demonstrate the impact of LCI-guided care on clinical decision-making and outcomes. This study was supported by the CF Foundation (#RATJEN22A0-LAD)
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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 | 0.011 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".