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Record W2076809598 · doi:10.2310/7070.2005.5007

Efficacy of a Leukotriene Receptor Antagonist in the Treatment of Perennial Allergic Rhinitis

2006· article· en· W2076809598 on OpenAlexvenueno aff
Rong‐San Jiang

Bibliographic record

VenueThe Journal of Otolaryngology · 2006
Typearticle
Languageen
FieldMedicine
TopicAllergic Rhinitis and Sensitization
Canadian institutionsnot available
Fundersnot available
KeywordsLoratadineMedicineAcoustic rhinometryrhinorrheaRhinomanometryDesloratadineAnesthesiaLeukotriene receptorZafirlukastAllergyPseudoephedrineTerfenadineDermatologyInternal medicineLeukotrieneNoseAsthmaSurgeryPharmacologyImmunology

Abstract

fetched live from OpenAlex

The objectives of this study were to investigate the efficacy of leukotriene receptor antagonists in the treatment of perennial allergic rhinitis. The study was designed as a randomized, 14-day treatment to compare the efficacy of zafirlukast, loratadine, and the combination of loratadine and pseudoephedrine in the treatment of perennial allergic rhinitis. Rhinitis symptom scores, acoustic rhinometry, and rhinomanometry were used to evaluate the efficacy. The results showed that after a 14-day treatment period, patients in all treatment groups had a lower mean score for the symptoms of rhinorrhea, nasal itching, and nasal obstruction (p < .05). Patients who took zafirlukast did not report a significant decrease in sneezing score (p = .1456), but the decrease in nasal obstruction score was more pronounced than in those who took loratadine or loratadine- pseudoephedrine (p = .014). However, the results of acoustic rhinometry and rhinomanometry did not have a significant difference among the three groups (p > .05). The study concluded that zafirlukast seemed to have a better effect on relieving the symptom of nasal obstruction in patients with perennial allergic rhinitis, but the actual mechanism needs further investigation.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.694
Threshold uncertainty score0.220

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.018
GPT teacher head0.267
Teacher spread0.249 · 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

Citations12
Published2006
Admission routes1
Has abstractyes

Explore more

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