MétaCan
Menu
Back to cohort
Record W2604136072 · doi:10.1186/s13223-017-0191-z

Nasal nitric oxide in allergic rhinitis in children and its relationship to severity and treatment

2017· article· en· W2604136072 on OpenAlexvenueno aff
Pengpeng Wang, Guixiang Wang, Wentong Ge, Lixing Tang, Jie Zhang, Xin Ni

Bibliographic record

VenueAllergy Asthma and Clinical Immunology · 2017
Typearticle
Languageen
FieldMedicine
TopicAllergic Rhinitis and Sensitization
Canadian institutionsnot available
FundersAllergopharmaCapital Medical UniversityEuropean Respiratory Society
KeywordsNitric oxideMedicineExhaled nitric oxideDermatologyAsthmaInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Nasal nitrous oxide (nNO) is increased in allergic rhinitis (AR), but not in asthma, and is a non-invasive marker for inflammation in the nasal passages. METHODS: Levels of nNO were measured and compared in healthy children and children with mild and moderate-to-severe AR. Levels of nNO before and after treatment with steroids and/or antihistamine were then compared in the 2 AR groups. Their relationship to quality of life and nasal symptom and reactivity to outdoor and outdoor allergens were examined. RESULTS: nNO levels were higher in mild AR than in healthy children and higher in moderate-to-severe AR than in mild AR. One month steroid and/or antihistamine treatment lowered nNO levels to control levels in mild AR and approximately halfway to control levels in moderate-to-severe AR. nNO levels had a weak correlation to quality of life questions and a fair correlation to nasal symptom scores before treatment. This correlation was weakened or lost after treatment, and no correlation was seen between nNO levels and responses to indoor or outdoor allergens. CONCLUSION: nNO levels in children with AR may be useful for assessing the response to treatment. Their relationship to quality of life, nasal symptoms, and sensitivity to specific allergens needs further study.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.647

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.033
GPT teacher head0.321
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations28
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueAllergy Asthma and Clinical ImmunologySame topicAllergic Rhinitis and SensitizationFrench-language works237,207