Feasibility of lung clearance index in a clinical setting in pre-school children
Bibliographic record
Abstract
Lung function testing in pre-school children in the clinical setting is challenging. Most cannot perform spirometry and many infant lung function tests require sedation. Lung clearance index (LCI) derived from the multiple-breath washout (MBW) test has been shown to be sensitive to early disease changes but may be time consuming and so a shortened test (LCI0.5) may be more feasible in young children. We sought to establish feasibility of MBW in unsedated pre-school children in a clinic setting and hypothesised use of LCI0.5would increase success rates. 116 pre-school children (28 healthy controls and 88 with respiratory disease), median age 4.0 years (range 2–6 years), underwent MBW tests unsedated in a clinic setting, using sulfur hexafluoride as a tracer gas and an adapted photoacoustic gas analyser. 81 (70%) out of 116 children completed LCI and 72% completed LCI0.5measurement. Test success increased significantly in patients over 3 years (0% at <2.5 years, 33% at 2.5–3 years and 70% at >3 years, p<0.0001). LCI was elevated in those with respiratory disease compared with healthy controls. MBW is feasible in a clinic setting in unsedated pre-schoolers, particularly in those >3 years old, and LCI is raised in those with respiratory disease. Use of LCI0.5did not increase success rate in pre-schoolers.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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 | 0.001 |
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".