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Record W2173184636 · doi:10.5539/gjhs.v8n5p239

Frequency of Bacterial Agents Isolated From Patients With Chronic Sinusitis in Northern Iran

2015· article· en· W2173184636 on OpenAlexvenueno aff
Rostam Pourmousa, Roksana Dadashzadeh, Fatemeh Ahangarkani, Mohammad Sadegh Rezai

Bibliographic record

VenueGlobal Journal of Health Science · 2015
Typearticle
Languageen
FieldMedicine
TopicSinusitis and nasal conditions
Canadian institutionsnot available
FundersMazandaran University of Medical Sciences
KeywordsMedicineSinusitisNasal cavityChronic sinusitisSinus (botany)PopulationAntibioticsMicrobiological cultureInternal medicineSurgeryBacteriaMicrobiologyBiologyEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: Sinusitis is a disease with significant health problems. Diagnosis of sinusitis is clinical. The golden standard for detection of microorganisms that cause sinusitis is the culture of sinus drainage discharge. OBJECTIVES: Due to the high prevalence of sinusitis in Iran, especially in Mazandaran province, in this study, bacteriological survey of patients with chronic sinusitis were done in order to help physicians in choosing better antibiotics for the empiric therapy of sinusitis. METHODS: This was a descriptive study. The population of the study consisted of 100 patients with chronic sinusitis caused by bacteria admitted to the Avicenna teaching hospital. Sampling for bacterial culture was performed by the endoscopy method from middle meatus (a curved anteroposterior passage in each nasal cavity that is situated below the middle nasal concha and extends along the entire superior border of the inferior nasal concha) and the opening of the maxillary sinus. Also sampling of nasal cavity was performed to determine the microbial flora. Identification of the bacteria causing chronic sinusitis was performed according to the standard microbiological procedures. Antimicrobial susceptibility testing method, the disk diffusion (Kirby-Bauer) was performed according to the CLSI (Clinical and Laboratory Standards Institute) standards. Data were analyzed using SPSS17 software. Also Fisher exact test and descriptive statistics were used to analyze the data. RESULTS: Among the 100 evaluated patients, 58% were male. The average age was 34.2±1.1. The most common complaint of patients were nasal congestion and post-nasal drip. The most common bacteria found in the nasopharynx were Gram-positive bacillus, coagulase negative Staphylococcus and Staphylococcus aureus with rates of 20%, 16% and 15% respectively. Bacteria isolated from opening sinus were Gram-positive bacillus 24%, Enterobacter aerogenes 10%, coagulase negative Staphylococcus 18% and Staphylococcus aureus 19%. CONCLUSIONS: Antibiotic prescription is often empiric in treatment of sinusitis. In our study resistance to some antibiotics such as penicillin subgroups that are used in treatment of chronic sinusitis was high. Due to the fact that the etiology of chronic sinusitis is not clearly understood, the frequency of all the common causative agents of this disease must be determined.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.029
GPT teacher head0.312
Teacher spread0.283 · 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 source (direct Gemma or distilled Codex), 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

Citations8
Published2015
Admission routes1
Has abstractyes

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