Identification and Classification of Risk Factors for Human-Robot Collaboration from a System-Wide Perspective

2021 ◽  
pp. 107827
Author(s):  
Nicole Berx ◽  
Wilm Decré ◽  
Ido Morag ◽  
Peter Chemweno ◽  
Liliane Pintelon
Author(s):  
Neill Y. Li ◽  
Alexander S. Kuczmarski ◽  
Andrew M. Hresko ◽  
Avi D. Goodman ◽  
Joseph A. Gil ◽  
...  

Abstract Introduction This article compares opioid use patterns following four-corner arthrodesis (FCA) and proximal row carpectomy (PRC) and identifies risk factors and complications associated with prolonged opioid consumption. Materials and Methods The PearlDiver Research Program was used to identify patients undergoing primary FCA (Current Procedural Terminology [CPT] codes 25820, 25825) or PRC (CPT 25215) from 2007 to 2017. Patient demographics, comorbidities, perioperative opioid use, and postoperative complications were assessed. Opioids were identified through generic drug codes while complications were defined by International Classification of Diseases, Ninth and Tenth Revisions, Clinical Modification codes. Multivariable logistic regressions were performed with p < 0.05 considered statistically significant. Results A total of 888 patients underwent FCA and 835 underwent PRC. Three months postoperatively, more FCA patients (18.0%) continued to use opioids than PRC patients (14.7%) (p = 0.033). Preoperative opioid use was the strongest risk factor for prolonged opioid use for both FCA (odds ratio [OR]: 4.91; p < 0.001) and PRC (OR: 6.33; p < 0.001). Prolonged opioid use was associated with an increased risk of implant complications (OR: 4.96; p < 0.001) and conversion to total wrist arthrodesis (OR: 3.55; p < 0.001) following FCA. Conclusion Prolonged postoperative opioid use is more frequent in patients undergoing FCA than PRC. Understanding the prevalence, risk factors, and complications associated with prolonged postoperative opioid use after these procedures may help physicians counsel patients and implement opioid minimization strategies preoperatively.


Author(s):  
Syahrun Neizam Mohd Dzulkifli ◽  
◽  
Abd Halid Abdullah ◽  
Yee Yong Lee ◽  
Mohd Mahathir Suhaimi Shamsuri ◽  
...  

2017 ◽  
Vol 7 (2) ◽  
pp. 200-211 ◽  
Author(s):  
Evan W. Matshes ◽  
Emma O. Lew

Recent evidence indicates that with thorough, high quality death investigations and autopsies, forensic pathologists have recognized that many unexpected infant deaths are, in fact, asphyxial in nature. With this recognition has come a commensurate decrease in, and in some cases, abolition of, the label “sudden infant death syndrome” (SIDS). Current controversies often pertain to how and why some infant deaths are determined to be asphyxial in nature and whether or not apparent asphyxial circumstances are risk factors for SIDS, or rather, harbingers of asphyxial deaths. In an effort to sidestep these controversies, some forensic pathologists elected to instead use the noncommittal label “sudden unexpected infant death” (SUID), leading to the unfortunate consequence of SUID – like SIDS – gaining notoriety as an actual disease that could be diagnosed, studied, and ultimately cured. Although it is not possible to provide death certification guidance for every conceivable type of unexpected infant death, we recognize and propose a simple classification system for overarching themes that cover the vast majority of cases where infants die suddenly and unexpectedly.


2014 ◽  
Vol 132 (4) ◽  
pp. 231-238 ◽  
Author(s):  
Larissa Santos Oliveira ◽  
Luiz Gustavo Oliveira Brito ◽  
Silvana Maria Quintana ◽  
Geraldo Duarte ◽  
Alessandra Cristina Marcolin

CONTEXT AND OBJECTIVE:Despite all the medical care provided during delivery labor, perineal injury is still prevalent and may lead to diverse pelvic floor disorders. The aim here was to investigate the prevalence of obstetric and anal sphincter injuries (OASIS) in healthy pregnant women after vaginal delivery.DESIGN AND SETTING:Cross-sectional study involving 3,034 patients with singletons in a secondary hospital for low-risk cases.METHODS:A standardized questionnaire was prepared and applied to medical files that had been completely filled out (classification of the Royal College of Obstetricians and Gynecologists, RCOG) in order to identify OASIS and analyze risk factors associated with mild and severe perineal lacerations.RESULTS:The women's mean age was 25 years; more than half (54.4%) were primiparae. Almost 38% of the participants had perineal lacerations; these were severe in 0.9% of the cases. Previous vaginal delivery (odds ratio, OR: 1.64 [1.33-2.04]) and forceps delivery (OR: 2.04 [1.39-2.97]) were risk factors associated with mild perineal injuries (1st and 2nd OASIS classifications). Only remaining standing for prolonged periods during professional activity (OR: 2.85 [1.34-6.09]) was associated with severe perineal injuries.CONCLUSION:The prevalence of severe perineal injuries was concordant with data in the literature. The variable of standing position was considered to be a risk factor for severe perineal injury and should be further investigated.


The Lancet ◽  
1980 ◽  
Vol 316 (8194) ◽  
pp. 586-587
Author(s):  
Joan Priestly ◽  
LuisH. Toledo-Pereyra ◽  
Marla Wohlman ◽  
Sidney Baskin

Author(s):  
Jong Yun Hwang

High-risk pregnancy is the probability of adverse pregnancy outcome is increased over the general pregnant population. Some high-risk pregnancy is the result of a medical condition present before pregnancy. In other cases, a medical condition that develops during pregnancy causes a pregnancy to become high risk. The reason why high-risk pregnancy is important is detecting the risk factors for high risk pregnancy early and preventing the complicated pregnancy. Korean society of Obstetrics and Gynecology (KSOG) announced the classification of high-risk pregnancy including 95 risk factors: obstetrics risk factors, medical risk factors, physical risk factors and risk factors of current pregnancy. However, this announcement of high-risk pregnancy by KSOG was limited for maternal-fetal healthcare providers to apply their working and making policy. First this didn't include the conception of the complicated pregnancy and high-risk delivery. Second this did not separate the risk factors depend on before and during pregnancy. This review briefly evaluates the classification of high-risk pregnancy by KSOG and suggest the new classification including the complicated pregnancy and high-risk delivery for maternal-fetal healthcare providers.


Circulation ◽  
2021 ◽  
Vol 143 (Suppl_1) ◽  
Author(s):  
Kelly Cho ◽  
Nicholas Link ◽  
Petra Schubert ◽  
Zeling He ◽  
Jacqueline P Honerlaw ◽  
...  

Introduction: The majority of population-based studies of myocardial infarction (MI) rely on billing codes for classification. Classification algorithms employing machine learning (ML) increasingly used for phenotyping using electronic health record (EHR) data. Hypothesis: ML algorithms integrating billing and information from narrative notes extracted using natural language processing (NLP) can improve classification of MI compared to billing code algorithms. Improved classification will improve power to compare risk factors across population subgroups. Methods: Retrospective cohort study of nationwide Veterans Affairs (VA) EHR data. MI classified using 2 approaches: (1) published billing code algorithm, (2) published phenotyping pipeline incorporating NLP and ML. Results compared against gold standard chart review of MI outcomes in 308 Veterans. We also tested known association between high density lipoprotein cholesterol (HDL-C) and MI outcomes classified using the 2 approaches among Black and White Veterans, stratified by sex and race; prior study showed HDL-C less protective for Black compared to White individuals. Results: We studied 17,176,658 million Veterans, mean age 69 years, 94% male, 12% self-report Black, 71% White. The billing code algorithm classified MI at positive predictive value (PPV) 0.64 compared to the published ML approach, PPV 0.90; the latter classified a modestly higher percentage of non-White Veterans. Using ML algorithm for MI, we replicated a reduced protective effect of HDL-C in Black vs White male and female Veterans (Table); with the billing code algorithm no association was observed between low density lipoprotein cholesterol (LDL-C) or HDL-C with MI among Black female Veterans. Conclusions: Using nationwide VA data, application of an ML approach improved classification of MI particularly among non-White Veterans, resulting in improved power to study differences in association for MI risk factors among Black and White Veterans.


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