Selecting Persistent Glue Ear for Referral in General Practice: A Risk Factor Approach

British Journal of General Practice（2002）SCI 2区SCI 1区

Cited 29|Views7

Abstract

Background: Glue ear (otitis media with effusion) is the most common reason for surgical intervention in children.Aim: To determine the yield and predictive value of a set of risk factors that predict persistence of glue ear over the interval from general practice referral to ear nose and throat (ENT) consultation to ensure the appropriateness of referrals.Design of study: Nested case control study.Setting: Sixteen ENT departments in the UK.Method: with the aid of audiometry and tympanometry, diagnostic information was collected on 548 children from 16 ENT departments after referral by their general practitioner (GP), as a lead-in to a clinical trial, the Mal of Alternative Regimens in Glue Ear Treatment (TARGET). Using cases and controls, children were classified as either having or not having persistent glue ear Parental reports on an extensive list of risk factors were also collected.Results: After adjustment for time waiting to he seen from GP referral and age at referral four main significant factors emerged for persistence of glue ear These were referral between July and December (OR [odds ratio] = 1. 73, 95% Cl = 1. 15 to 2.6); having a mother who smokes ten or more cigarettes per day (OR = 1. 7 9596 Cl = 1. 1 to 2.8); multiple upper airway symptoms (OR = 2.2, 9596 Cl = - 1. 5 to 3.2; and siblings with a history of glue ear (OR = 1. 6 for one sibling versus none).Conclusion: For a child who is referred between July and December who has two or more upper airway symptoms, who has a sibling who has had glue ear and who has a mother who smokes ten or more cigarettes per day, the odds of having persistent glue ear are over ten times that of a child without adverse values on these factors.

Translated text

Key words

risk factors,glue ear,otitis media with effusion,middle ear,child health

求助PDF

上传PDF

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Upload PDF to Generate Summary

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper