Iranian females report more severe symptoms on the SNOT-22: A cross-sectional population-based study

Our results revealed a relatively higher prevalence of abnormal SNOT-22 scores, especially among women. Therefore, designing and implementing therapeutic and educational interventions for these people seems necessary.

Authors

Institutions

Publication Details

Journal
American Journal of Otolaryngology
Published
2025-04-22
DOI
https://doi.org/10.1016/j.amjoto.2025.104631
Primary Topic
Sinusitis and nasal conditions
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Iranian females report more severe symptoms on the SNOT-22: A cross-sectional population-based study

Zahra Rahimi, Seyed Mohammad Tabibzadeh, Sara Saki, Bahman Cheraghian et al.
American Journal of Otolaryngology
Sinusitis and nasal conditions
article

Iranian females report more severe symptoms on the SNOT-22: A cross-sectional population-based study

Zahra Rahimi, Seyed Mohammad Tabibzadeh, Sara Saki, Bahman Cheraghian, Nader Saki, Soheila Nikakhlagh
article en

Abstract

Our results revealed a relatively higher prevalence of abnormal SNOT-22 scores, especially among women. Therefore, designing and implementing therapeutic and educational interventions for these people seems necessary.

American Journal of OtolaryngologyVol. 46(4)
Ahvaz Jundishapur University of Medical Sciences (IR), Palo Alto University (US), Stanford Medicine (US), Stanford University (US)
Ahvaz Jundishapur University of Medical Sciences
Openalex Percentile: Top 89%
Sinusitis and nasal conditions
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.