Observed to expected or logistic regression to identify hospitals with high or low 30-day mortality?

about

Observed to expected or logistic regression to identify hospitals with high or low 30-day mortality?

Item

http://www.wikidata.org/entity/Q53279736

description

2018 nî lūn-bûn

@nan

2018年の論文

@ja

2018年学术文章

@wuu

2018年学术文章

@zh

2018年学术文章

@zh-cn

2018年学术文章

@zh-hans

2018年学术文章

@zh-my

2018年学术文章

@zh-sg

2018年學術文章

@yue

2018年學術文章

@zh-hant

name

Observed to expected or logist ...... high or low 30-day mortality?

@en

Observed to expected or logist ...... high or low 30-day mortality?

@nl

type

Item

label

Observed to expected or logist ...... high or low 30-day mortality?

@en

Observed to expected or logist ...... high or low 30-day mortality?

@nl

prefLabel

Observed to expected or logist ...... high or low 30-day mortality?

@en

Observed to expected or logist ...... high or low 30-day mortality?

@nl

P1476

Observed to expected or logist ...... high or low 30-day mortality?

@en

P2093

Jocelyne Clench-Aas

http://www.w3.org/2001/XMLSchema#string

Jon Helgeland

http://www.w3.org/2001/XMLSchema#string

Petter Laake

http://www.w3.org/2001/XMLSchema#string

P2860

The Measurement of Observer Agreement for Categorical Data

Differences in case-mix can influence the comparison of standardised mortality ratios even with optimal risk adjustment: an analysis of data from paediatric intensive care.

30-Day Survival Probabilities as a Quality Indicator for Norwegian Hospitals: Data Management and Analysis

When things go wrong: the impact of being a statistical outlier in publicly reported coronary artery bypass graft surgery mortality data.

Hospital standardized mortality ratio: consequences of adjusting hospital mortality with indirect standardization

Survival curves to support quality improvement in hospitals with excess 30-day mortality after acute myocardial infarction, cerebral stroke and hip fracture: a before-after study.

What is the best way to estimate hospital quality outcomes? A simulation approach.

A comparison of regression trees, logistic regression, generalized additive models, and multivariate adaptive regression splines for predicting AMI mortality.

Bias in estimates of confidence intervals for health outcome report cards.

The Impact of Hospital Size on CMS Hospital Profiling.

P304

e0195248

http://www.w3.org/2001/XMLSchema#string

P31

scholarly article

P356

10.1371/JOURNAL.PONE.0195248

http://www.w3.org/2001/XMLSchema#string

P407

English

P433

http://www.w3.org/2001/XMLSchema#string

P478

http://www.w3.org/2001/XMLSchema#string

P50

Marit B. Veierød

Doris Tove Kristoffersen

P577

2018-04-13T00:00:00Z

http://www.w3.org/2001/XMLSchema#dateTime

P698

29652941

http://www.w3.org/2001/XMLSchema#string

P921

logistic regression model

P932

5898724

http://www.w3.org/2001/XMLSchema#string