Bibliographic record
Abstract
OBJECTIVE: To report on airway endoscopic findings and gastrointestinal and atopic conditions in a large consecutive series of atypical croup. STUDY DESIGN: Case series with chart review. SETTING: Tertiary pediatric referral center. SUBJECTS AND METHODS: A surgical database was searched for all children who underwent full airway endoscopy to investigate atypical croup. The primary outcome measure was the prevalence of large airway lesions in patients with atypical croup undergoing endoscopy. Demographics, secondary diagnoses, and rate of positive findings were documented. Age and atopy were correlated using Spearman's correlation coefficient, and multivariate analysis identified predictors of large airway lesions. RESULTS: Eighty patients were identified over a period of 8 years (58 boys; mean [SD] age 4.8 [3.8] years; range, 46 days to 13.7 years). Of the 80 children, 31 had positive airway findings, with 33 large airway lesions demonstrated, including 10 subglottic stenosis, 7 laryngeal clefts, 6 subglottic hemangiomas, 4 tracheomalacia, and 3 laryngomalacia. Esophagitis was diagnosed in 36 children, 5 of whom had eosinophilic esophagitis. Thirty-five children had an atopic condition including asthma, allergic rhinitis, eosinophilic esophagitis, and food allergies. Age correlated with associated atopy (coefficient 0.4, P < .0001) and predicted the presence of any airway lesion (coefficient -0.0625, P < .001) and subglottic stenosis in particular (coefficient -0.0362, P = .001). Prior intubation predicted subglottic stenosis (coefficient 0.267, P = .011). CONCLUSION: Thirty-nine percent of airway endoscopies demonstrated large airway lesions. When eosinophilic esophagitis was sought, it was confirmed in over 1:10 patients. The findings bolster the case for airway endoscopy coupled with allergy and gastrointestinal investigations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".