Lung clearance index is increased in high-risk preschool wheezers and is associated with asthma control
Bibliographic record
Abstract
Introduction: There is currently no diagnostic test for asthma and objective measures to assess response to therapy in children under 6 years. The lung clearance index (LCI) is a marker of ventilation inhomogeneity derived from the multiple breath washout (MBW) test that is sensitive to small airway changes. We aimed to determine if LCI matched asthma risk and reflected clinical improvement after a respiratory exacerbation. Methods: Children aged 3 to 6 years with recurrent wheeze were recruited from the Emergency Department at the Hospital for Sick Children in case of an asthma exacerbation. MBW testing (Exhalyzer D, Ecomedics), skin prick tests to a panel of inhalant and food allergens and parental questionnaires (Test for Respiratory and Asthma Control in Kids (TRACK), Asthma Predictive Index (API)) were performed. Twelve weeks later testing was repeated. The risk to develop asthma was defined based on API. Change in LCI between follow-up and baseline visits were compared in relation to TRACK scores. Results: LCI was increased in children at high-risk for asthma (n=23) compared with children with low-risk (n=18) (high-risk: 7.96 ± 0.49; low-risk: 7.47 ± 0.88; p <0.05). Mean LCI at follow-up visit was lower compared to baseline (n=18) (baseline: 9.43 ± 1.75; follow-up: 8.13 ± 0.85). Mixed-effect models demonstrated a significant decrease in LCI at follow-up compared to baseline (estimate -1.55 units; 95% CL: -2.4137, -0.6874; p <0.001) after adjusting for atopy and height. The concordance of a decrease in LCI and clinically-relevant improvement in TRACK score was 67% (12/18). Conclusions LCI may be sensitive to discriminate preschool asthma and for monitoring asthma control.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".