The presence of Interleukin-13 in nasal lavage may be a predictor of nasal polyposis in pediatric patients with cystic fibrosis
Bibliographic record
Abstract
BACKGROUND: Sinonasal disease is a common feature of cystic fibrosis (CF) and can cause significant morbidity in these patients. Our objective was to determine if CF individuals with concomitant nasal polyposis (NP) express a unique profile of inflammation and if so, whether these inflammatory cytokine mediators have predictive value in identifying these individuals for prompt management by an Otolaryngologist. METHODOLOGY: Nasal lavage samples and clinical outcomes of disease severity were obtained from thirty-eight pediatric CF individuals. Participants were subdivided based on the presence or absence of NP. Nasal lavage samples were analyzed on a panel of seventeen cytokine targets using a Bio-Plex Luminex assay. A Perl Permutation test with correction for multiple hypotheses was performed to identify uniquely expressed cytokines between CF individuals with NP (CFwNP) and those without (CFsNP). RESULTS: Thirty-five patients were included in the analysis. Cytokines IL-13 and GM-CSF were uniquely expressed in the CFwNP group when compared to the CFsNP group. Logistic regression analysis demonstrated a significant association of IL-13 with NP. CONCLUSION: In children diagnosed with CF, the level of IL-13 in nasal lavage samples could potentially serve as a non-invasive clinical tool in predicting NP in this population, and a target for future immunotherapy.
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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) | 0.000 | 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) | 0.001 | 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".