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Text
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URL Address
<a href="http://doi.org/10.1177/0883073807309254" target="_blank" rel="noreferrer">http://doi.org/10.1177/0883073807309254</a>
Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Title
A name given to the resource
Diagnostic yield of brain biopsies in children presenting to neurology
Publisher
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Journal Of Child Neurology
Date
A point or period of time associated with an event in the lifecycle of the resource
2008
Subject
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Child; Female; Humans; Male; Odds Ratio; Predictive Value of Tests; Outcome and Process Assessment (Health Care); Preschool; infant; retrospective studies; Brain/pathology; Diagnosis; Differential; Children W/SNI; Epilepsy/pathology; Likelihood Functions; Decision Trees; Biopsy/statistics & numerical data; Brain Diseases/pathology; Neurodegenerative Diseases/pathology; Vasculitis/pathology
Creator
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Venkateswaran S; Hawkins C; Wassmer E
Description
An account of the resource
The role of brain biopsy is well established in patients with neoplastic lesions, with a diagnostic yield approaching 95%. The diagnostic yield of brain biopsy in adults with neurological decline varies from 20% to 43%. Only a few studies have examined the diagnostic yield of brain biopsy in children with idiopathic neurological decline. A retrospective analysis was conducted on all open and closed pediatric brain biopsies performed between January 1988 and May 2003. Biopsies were performed for diagnostic purposes in patients showing a progressively deteriorating neurologic course in whom less-invasive modalities such as neuroimaging, electroencephalography (EEG), and molecular genetic studies were either negative or inconclusive. Immunocompromised patients were included. Patients were excluded if the preoperative diagnosis was a neoplasm or if the patient was undergoing a resection as part of a work-up for intractable epilepsy. Each patient underwent numerous investigations before brain biopsy. The utility of each biopsy was analyzed. Sixty-six children had brain biopsies performed for diagnostic purposes during the study period. Patient ages ranged from 2 months to 16 years and 9 months at the time of biopsy. The diagnostic yield was 48.5% overall, with a yield of 68.8% between 1996 and 2003. Of the total, 26 (39.4%) biopsies were both diagnostic and useful. Patients most frequently presented with seizures (56.1%) and encephalopathy (33%). The most frequently diagnosed disease was vasculitis (18.2%). A total of 71.9% of patients with diagnostic biopsies improved with appropriate treatment. Brain biopsy in children had a diagnostic yield of 48.5% in our series. A specific diagnosis may help in management and outcome, especially with a diagnosis of vasculitis.
2008
Identifier
An unambiguous reference to the resource within a given context
<a href="http://doi.org/10.1177/0883073807309254" target="_blank" rel="noreferrer">10.1177/0883073807309254</a>
Rights
Information about rights held in and over the resource
Article information provided for research and reference use only. PedPalASCNET does not hold any rights over the resource listed here. All rights are retained by the journal listed under publisher and/or the creator(s).
Type
The nature or genre of the resource
Journal Article
2008
Backlog
Biopsy/statistics & numerical data
Brain Diseases/pathology
Brain/pathology
Child
Children W/SNI
Decision Trees
Diagnosis
Differential
Epilepsy/pathology
Female
Hawkins C
Humans
Infant
Journal Article
Journal of Child Neurology
Likelihood Functions
Male
Neurodegenerative Diseases/pathology
Odds Ratio
Outcome And Process Assessment (health Care)
Predictive Value of Tests
Preschool
Retrospective Studies
Vasculitis/pathology
Venkateswaran S
Wassmer E
-
Text
A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text.
Citation List Month
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URL Address
<a href="http://doi.org/10.1097/00003246-200103000-00035" target="_blank" rel="noreferrer">http://doi.org/10.1097/00003246-200103000-00035</a>
Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Title
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Long-stay patients in the pediatric intensive care unit
Publisher
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Critical Care Medicine
Date
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2001
Subject
The topic of the resource
Child; Female; Humans; Male; Intensive Care Units; Hospital Mortality; Logistic Models; Treatment Outcome; Comorbidity; Health Services Research; Severity of Illness Index; Risk Factors; Quality of Health Care; Sensitivity and Specificity; Analysis of Variance; Predictive Value of Tests; Cost Savings; Preschool; infant; algorithms; Pediatric/utilization; ICU Decision Making; United States/epidemiology; Age Distribution; Discriminant Analysis; Patient Admission/statistics & numerical data; Length of Stay/statistics & numerical data; Emergencies; Decision Trees; Intensive Care/economics/standards
Creator
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Marcin JP; Slonim AD; Pollack MM; Ruttimann UE
Description
An account of the resource
OBJECTIVE: Length of stay in the pediatric intensive care unit (PICU) is a reflection of patient severity of illness and health status, as well as PICU quality and performance. We determined the clinical profiles and relative resource use of long-stay patients (LSPs) and developed a prediction model to identify LSPs for early quality and cost saving interventions. DESIGN: Nonconcurrent cohort study. SETTING: A total of 16 randomly selected PICUs and 16 volunteer PICUs. PATIENTS: A total of 11,165 consecutive admissions to the 32 PICUs. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: LSPs were defined as patients having a length of stay greater than the 95th percentile (>12 days). Logistic regression analysis was used to determine which clinical characteristics, available within the first 24 hrs after admission, were associated with LSPs and to create a predictive algorithm. Overall, LSPs were 4.7% of the population but represented 36.1% of the days of care. Multivariate analysis indicated that the following factors are predictive of long stays: age <12 months, previous ICU admission, emergency admission, no CPR before admission, admission from another ICU or intermediate care unit, chronic care requirements (total parenteral nutrition and tracheostomy), specific diagnoses including acquired cardiac disease, pneumonia, and other respiratory disorders, having never been discharged from the hospital, need for ventilatory support or an intracranial catheter, and a Pediatric Risk of Mortality III score between 10 and 33. The performance of the prediction algorithm in both the training and validation samples for identifying LSPs was good for both discrimination (area under the receiver operating characteristics curve of 0.83 and 0.85, respectively), and calibration (goodness of fit, p = .33 and p = .16, respectively). LSPs comprised from 2.1% to 8.1% of individual ICU patients and occupied from 15.2% to 57.8% of individual ICU bed days. CONCLUSIONS: LSPs have less favorable outcomes and use more resources than non-LSPs. The clinical profile of LSPs includes those who are younger and those that require chronic care devices. A predictive algorithm could help identify patients at high risk of prolonged stays appropriate for specific interventions.
2001
Identifier
An unambiguous reference to the resource within a given context
<a href="http://doi.org/10.1097/00003246-200103000-00035" target="_blank" rel="noreferrer">10.1097/00003246-200103000-00035</a>
Rights
Information about rights held in and over the resource
Article information provided for research and reference use only. PedPalASCNET does not hold any rights over the resource listed here. All rights are retained by the journal listed under publisher and/or the creator(s).
Type
The nature or genre of the resource
Journal Article
2001
Age Distribution
algorithms
Analysis of Variance
Backlog
Child
Comorbidity
Cost Savings
Critical Care Medicine
Decision Trees
Discriminant Analysis
Emergencies
Female
Health Services Research
Hospital Mortality
Humans
ICU Decision Making
Infant
Intensive Care Units
Intensive Care/economics/standards
Journal Article
Length Of Stay/statistics & Numerical Data
Logistic Models
Male
Marcin JP
Patient Admission/statistics & numerical data
Pediatric/utilization
Pollack MM
Predictive Value of Tests
Preschool
Quality Of Health Care
Risk Factors
Ruttimann UE
Sensitivity and Specificity
Severity Of Illness Index
Slonim AD
Treatment Outcome
United States/epidemiology