Limitations of regional ventilation inhomogeneity indices in children with cystic fibrosis
Bibliographic record
Abstract
Abstract Background Scond is a multiple breath washout (MBW) index that measures convection‐dependent ventilation inhomogeneity (CDI) arising within conductive airways, but the calculation method is unreliable in subjects with advanced cystic fibrosis (CF) lung disease. A new CDI index, Scond*, has been proposed for use in adults with CF and moderate to severe ventilation inhomogeneity. We aimed to evaluate the most appropriate CDI index in children and adolescents with CF and various degrees of inhomogeneity, and from that the most appropriate diffusion‐convection‐interaction index (Sacin or Sacin*). Methods Scond, Sacin and the alternative indices, Scond*, and Sacin* were retrospectively calculated in subjects with CF aged 3 to 18 years and age‐matched controls, who underwent sulfur hexafluoride MBW between 2003 and 2015. The upper limit of normal was based on 95th percentile of the control population. Results One hundred and twenty‐seven subjects with CF (44% male; mean age ± SD: 7.5 years ± 4.9) and 94 controls (53% male; 7.9 years ± 5.1) were included in the final analysis. All measures of ventilation inhomogeneity were significantly higher in children with CF. As predicted, Scond reached a maximum value at lung clearance index (LCI) values of approximately 9. In subjects with LCI ≥ 9 Scond* showed good correlation with LCI, whilst Scond had no relationship with LCI (Spearman rank correlation Scond*/LCI, 0.49; P < .01; Scond/LCI, −0.068; P = .46). In subjects with mild disease (LCI < 9) Scond was more frequently abnormal than Scond* (37% vs 16%; P = .01). Conclusions Scond and Sacin are sensitive indices of early regional inhomogeneity, but are of no value when LCI ≥ 9. In these subjects, Scond* & Sacin* are potential alternatives.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".