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Identifying patients at high risk of emergency hospital admissions: a logistic regression analysis

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title

Identifying patients at high risk of emergency hospital admissions: a logistic regression analysis (English)

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main subject

patient

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logistic regression model

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based on heuristic

inferred from title

logistic regression analysis

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Alex Bottle

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Paul Aylin

object named as

Paul Aylin

series ordinal

2

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Azeem Majeed

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Azeem Majeed

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3

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language of work or name

English

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publication date

August 2006

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published in

Journal of the Royal Society of Medicine

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volume

99

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issue

8

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page(s)

406-14

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cites work

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PubMed Central

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Rising to the challenge: will the NHS support people with long term conditions?

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2 November 2018

Follow up of people aged 65 and over with a history of emergency admissions: analysis of routine admission data

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2 November 2018

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International trends in the provision and utilisation of hospital care

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2 November 2018

Deprivation and emergency admissions for cancers of colorectum, lung, and breast in south east England: ecological study

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2 November 2018

Hospital admissions for asthma in east London: associations with characteristics of local general practices, prescribing, and population

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Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors

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2 November 2018

Preventable Hospitalizations and Access to Health Care

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2 November 2018

Hospital episode statistics: time for clinicians to get involved?

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reference URL

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retrieved

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based on heuristic

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What value do consultant nurses place on the the White Paper, Our Health, Our Care, Our Say: a New Direction For Community Services (DH 2006A)?

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reference URL

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retrieved

12 December 2020

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Identifiers

DOI

10.1258/JRSM.99.8.406

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PMC publication ID

1533524

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PubMed publication ID

16893941

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(Q57274533)

Identifying patients at high risk of emergency hospital admissions: a logistic regression analysis

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