Machine learning in burn care and research: A systematic review of the literature

Liu, Nehemiah T.; Salinas, José

doi:10.1016/j.burns.2015.07.001

Cited by 33 publications

(25 citation statements)

References 35 publications

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“…Independent scrutiny of the titles and abstracts by two authors (JW; ZT) identified 119 potentially relevant articles. Of those, we excluded an additional 59 studies because they failed to meet the methodological definition of systematic review as per our inclusion criteria . Therefore, a total of 60 systematic reviews formed the basis of this study …”

Section: Resultsmentioning

confidence: 99%

Poor methodological quality and reporting standards of systematic reviews in burn care management

Wasiak

Tyack

Ware

et al. 2016

International Wound Journal

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The methodological and reporting quality of burn-specific systematic reviews has not been established. The aim of this study was to evaluate the methodological quality of systematic reviews in burn care management. Computerised searches were performed in Ovid MEDLINE, Ovid EMBASE and The Cochrane Library through to February 2016 for systematic reviews relevant to burn care using medical subject and free-text terms such as 'burn', 'systematic review' or 'meta-analysis'. Additional studies were identified by hand-searching five discipline-specific journals. Two authors independently screened papers, extracted and evaluated methodological quality using the 11-item A Measurement Tool to Assess Systematic Reviews (AMSTAR) tool and reporting quality using the 27-item Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist. Characteristics of systematic reviews associated with methodological and reporting quality were identified. Descriptive statistics and linear regression identified features associated with improved methodological quality. A total of 60 systematic reviews met the inclusion criteria. Six of the 11 AMSTAR items reporting on 'a priori' design, duplicate study selection, grey literature, included/excluded studies, publication bias and conflict of interest were reported in less than 50% of the systematic reviews. Of the 27 items listed for PRISMA, 13 items reporting on introduction, methods, results and the discussion were addressed in less than 50% of systematic reviews. Multivariable analyses showed that systematic reviews associated with higher methodological or reporting quality incorporated a meta-analysis (AMSTAR regression coefficient 2.1; 95% CI: 1.1, 3.1; PRISMA regression coefficient 6·3; 95% CI: 3·8, 8·7) were published in the Cochrane library (AMSTAR regression coefficient 2·9; 95% CI: 1·6, 4·2; PRISMA regression coefficient 6·1; 95% CI: 3·1, 9·2) and included a randomised control trial (AMSTAR regression coefficient 1·4; 95%CI: 0·4, 2·4; PRISMA regression coefficient 3·4; 95% CI: 0·9, 5·8). The methodological and reporting quality of systematic reviews in burn care requires further improvement with stricter adherence by authors to the PRISMA checklist and AMSTAR tool.

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Section: Resultsmentioning

confidence: 99%

Poor methodological quality and reporting standards of systematic reviews in burn care management

Wasiak

Tyack

Ware

et al. 2016

International Wound Journal

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“…A recent report of the use of machine learning in burn care and research returned 15 retrospective observation studies related to: burn diagnosis and healing (n = 5), antibacterial response in the burn wound (n = 4), hospital length of stay (n = 2), and mortality (n = 4). [ 39 ] Although algorithm performance was assessed differently in each report, all demonstrated improved performance of machine learning over traditional biostatistics methodologies. These techniques may be particularly useful for determining the impacts of depth and distribution of burn injury on long-term health and function.…”

Section: Discussionmentioning

confidence: 99%

Mortality prognostication scores do not predict long-term, health-related quality of life after burn: A burn model system national database study

Stewart

Carrougher

Curtis

et al. 2021

Burns

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Highlights There are few tools to facilitate shared decision-making after burn injury. Mortality prognostication scores are thought to reflect long-term quality of life. Higher rBaux and Ryan scores are negatively correlated with long-term physical health, but not mental health. Burn size and mortality prognostication scores poorly explained the variance quality of life. More accurate scores are needed to predict long-term quality of life

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“…La esperanza de disponer de conclusiones significativas en la inmensidad de información médica y terapéutica alcanzada se va vislumbrando mediante el uso de inteligencias artificiales [70] y el uso del aprendizaje automático (machine learning), dado que permite un rendimiento superior a los métodos estadísticos tradicionales [71], [72]. Por ejemplo, m-fasis incorpora todos los marcos analíticos de lesiones deportivas anteriores y las cuentas de lesiones o afecciones que sanan o no sanan al 100%, lesiones agudas y por uso excesivo, enfermedades y resultados de eventos competitivos, lo cual permite elegir de manera más transparente respecto al resultado y el tiempo de curación de una lesión [73].…”

Section: Impacto De Las Nuevas Tecnologías En La Reparación De Las Leunclassified

Revisión normativa y conceptual relacionada con la incapacidad medicolegal en Colombia

Támara-Patiño¹,

Fontanilla-Duque²

2019

Colomb. forense

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Propósito: actualmente, el avance médico tecnológico en diagnóstico, terapéuticas y rehabilitación exige la revisión de los parámetros de la valoración medicolegal de las personas que acuden al sector justicia colombiano. Para ello, se recopila información normativa y conceptual sobre las lesiones no fatales, especialmente en lo relacionado con los tiempos de reparación y los factores que los afectan. Periodo que se abarcó, origen y tipos de publicaciones: el periodo abarcado para el análisis conceptual es del año 2006 al 2018, con artículos recopilados de PubMed y Science Direct sin ninguna preferencia en cuanto al origen. Se realiza revisión legislativa nacional relacionada. Punto de vista: aunque la determinación de la reparación de las lesiones se establecía como punto de partida para definir las incapacidades medicolegales, su variabilidad y el impacto de las nuevas tecnologías en salud amerita resignificar las formas de medir estos tiempos de manera individualizada. Conclusiones: es imprescindible avanzar en estudios de investigación en nuestros grupos poblacionales, que permitan tener información para el sector forense.

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Machine learning in burn care and research: A systematic review of the literature

Cited by 33 publications

References 35 publications

Poor methodological quality and reporting standards of systematic reviews in burn care management

Poor methodological quality and reporting standards of systematic reviews in burn care management

Mortality prognostication scores do not predict long-term, health-related quality of life after burn: A burn model system national database study

Revisión normativa y conceptual relacionada con la incapacidad medicolegal en Colombia

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