Relationship between phenotypic characteristics of children with sickle cell disease and airway nitric oxide levels
Bibliographic record
Abstract
Background: Children with sickle cell disease (SCD) have evidence of airflow limitation on spirometry, and evidence for increased airway nitric oxide (NO) levels. Objective: To determine the relationship between phenotypic characteristics (atopy, asthma, acute chest syndrome (ACS), pulmonary function test abnormalities) and airway NO levels in children with SCD. Methods: Children with SCD were recruited during visits for routine annual pulmonary function testing. Subjects completed ISAAC/medical questionnaires, skin prick testing for aeroallergen sensitization, and pulmonary function testing. Nasal (NNO) and the fraction of exhaled NO (FENO) were measured. Subjects were classified by phenotypic characteristics, and NNO and FENO compared between groups, using Mann-Whitney tests. Results: 154 children participated. Seventy-six (49%) were male. Mean(±SD) age was 12.6±3.3 years. Hemoglobin levels were 96.0±18.3 g/L. FVC, FEV 1 , and FEF 25-75 were 86.7±14.0, 84.7±15.0, and 84.4±30.0 percent predicted, respectively. NNO and FENO were 890±387 and 17.9±15.9 ppb. Eighty (52%) subjects were atopic. Thirteen (8%) subjects taking asthma medication, and 38 (25%) with an asthma diagnosis or wheezing, were classified as asthmatic. Twenty (13%) subjects had a significant bronchodilator response (≥12%) in FEV 1 or FVC. Seventy-three (47%) subjects reported previous ACS or pneumonia. No significant differences were seen in NNO or FENO between any groups compared. Conclusions: Airway NO measurements did not correlate with any of the phenotypic characteristics evaluated in this cohort of children with SCD, and therefore may not be a meaningful tool in the assessment of SCD lung disease.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".