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Medical consultations depend on a shared linguistic understanding between the patient and physician. When language concordance is not possible, interpretation is required. Prior studies have revealed that professional in-person interpretation (PIPI) results in patients reporting higher satisfaction and a better understanding of things the physician explained. Despite this, language-discordance often results in using family and/or friends for ad hoc interpretation. This systematic review examines the linguistic aspect of medical interpretation by assessing the number of linguistic errors made and their relation to professional in-person interpretation (PIPI) or in-person ad hoc interpretation (IPAHI). PIPI was defined as people employed as interpreters, but with no specific requirements for education or experience. This systematic review examines studies comparing the number of errors when using PIPI and IPAHI. We performed a PICO-criteria-based search in five scientific databases. We screened English and Danish studies published between 1995 and October 2024. Furthermore, we screened references from, and citations of the included articles. We used the appropriate Cochrane Tool for risk of bias assessment. We identified six studies using a PICO search and one additional study by snowballing. The included studies revealed critical methodological differences, and consequently a statistical synthesis of results was not conducted. We found indications that the number of interpreting errors was significantly lower when using PIPI than family members for IPAHI. Interpreting error rates were not significantly lower when comparing PIPI to the use of medical staff without interpretation training for IPAHI. Generally, we found that the difference between PIPI and IPAHI tended to be more prominent when dealing with more severe diagnoses, e.g., incurable cancer. The methodological differences between included studies and the risk of bias within included studies limit the conclusions drawn in this review. Also, no other kinds of interpretation than PIPI and IPAHI were considered, and the recommendations are solely based on accuracy. Considering these limitations and the fact that no other systematic reviews within this highly specific topic exist, this review resulted in the following recommendations: 1) Professional in-person interpretation should be the first choice in language-discordant medical consultations. 2) If professional interpretation is not possible, using medical staff without interpretation training should be chosen before interpretation by family or friends. 3) All consultation participants should keep sentences short and straightforward, as this is related to a lower risk of omissions in interpretation.
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http://dx.doi.org/10.1186/s13690-024-01461-8 | DOI Listing |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11653638 | PMC |
Biometrics
October 2024
Evidence Generation and Advanced Analytics Biogen Digital Health, Biogen, Cambridge, MA 02142, United States.
In many clinical contexts, the event of interest could occur multiple times for the same patient. Considerable advancement has been made on developing recurrent event models based on or that use biomarker information. However, less attention has been given to evaluating the prognostic accuracy of a biomarker or a composite score obtained from a fitted recurrent event-rate model.
View Article and Find Full Text PDFBiometrics
October 2024
Department of Population Health Sciences, University of Utah School of Medicine, Salt Lake City, UT 84108, United States.
Many trials are designed to collect outcomes at or around pre-specified times after randomization. If there is variability in the times when participants are actually assessed, this can pose a challenge to learning the effect of treatment, since not all participants have outcome assessments at the times of interest. Furthermore, observed outcome values may not be representative of all participants' outcomes at a given time.
View Article and Find Full Text PDFAnn Ital Chir
December 2024
Department of Colorectal Surgery, Hubei Provincial Hospital of Traditional Chinese Medicine Affiliated to Hubei University of Chinese Medicine, 430071 Wuhan, Hubei, China.
Aim: Anorectal diseases, often requiring surgical intervention and careful post-operative wound management, pose substantial challenges in healthcare. This study presents a novel application of artificial intelligence, specifically machine learning, aimed at improving the classification and analysis of post-surgical wound images. By doing so, it seeks to enhance patient outcomes through personalized and optimized wound care strategies.
View Article and Find Full Text PDFBr J Math Stat Psychol
December 2024
Athens University of Economics and Business, Athens, Greece.
This paper introduces the generalized Hausman test as a novel method for detecting the non-normality of the latent variable distribution of the unidimensional latent trait model for binary data. The test utilizes the pairwise maximum likelihood estimator for the parameters of the latent trait model, which assumes normality of the latent variable, and the maximum likelihood estimator obtained under a semi-non-parametric framework, allowing for a more flexible distribution of the latent variable. The performance of the generalized Hausman test is evaluated through a simulation study and compared with other test statistics available in the literature for testing latent variable distribution fit and an overall goodness-of-fit test statistic.
View Article and Find Full Text PDFJ Comput Chem
January 2025
Department of Chemistry, Birla Institute of Technology Mesra, Ranchi, India.
Accurate prediction of physicochemical properties, such as electronic energy, enthalpy, free energy, and average vibrational frequencies, is critical for optimizing lithium-ion battery (LIB) performance. Traditional methods like density functional theory (DFT) are computationally expensive and inefficient for large-scale screening. In this study, we apply active learning on top of graph neural networks (GNNs) to efficiently predict these properties.
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