Late Breaking Abstract - AI-based mucus plug analysis to evaluate the effect of inhaled hypertonic saline in preschool children with CF: a randomized controlled trial
Bibliographic record
Abstract
Background: SHIP-CT evaluated the impact of inhaled hypertonic saline(HS) vs isotonic saline(IS) on lung structure in preschool children with CF(pCwCF). PRAGMA-CF scoring and automatic bronchus-artery analysis showed fewer bronchial abnormalities in the HS group compared to IS after 48 weeks. A recently developed AI-algorithm allows for quantification of mucus plugs(MP) on CT. This analysis aimed to assess the effect of inhaled HS on MP in pCwCF. Methods: Number, volume, and location of MP throughout the bronchial tree were quantified using a fully automatic algorithm(LungQ, Thirona). It identifies MP that completely obstruct the bronchus with a detectable proximal lumen. Regression analysis was used to evaluate the difference between groups at 48 weeks. Results: 113 baseline and 103 48-week CTs among 115 pCwCF(55 HS,60 IS) were analyzed. MPs were detected in 37% and 25% of CTs at baseline and 48 weeks, respectively. In patients with MP, median(IQR) MP count at baseline and at 48 weeks was 1(1.5) and 4(7) for the IS group, and 2(6.5) and 1.5(27.2) for the HS group. At 48 weeks, the IS group showed a higher MP count than the HS group(mean difference 0.75; 95%CI 0.36–1.15; p = 0.0005). Conclusion: Inhaled hypertonic saline significantly reduced MP over 48 weeks in preschool children with CF, supporting previous findings of improved structural airway abnormalities in the HS group. erj;66/suppl_69/OA3321/F1 F1 F1
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".