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Introduction: The goal of disaster triage at both the prehospital and in-hospital level is to maximize resources and optimize patient outcomes. Of the disaster-specific triage methods developed to guide health care providers, the Simple Triage and Rapid Treatment (START) algorithm has become the most popular system world-wide. Despite its appeal and global application, the accuracy and effectiveness of the START protocol is not well-known. Objectives: The purpose of this meta-analysis was two-fold: (1) to estimate overall accuracy, under-triage, and over-triage of the START method when used by providers across a variety of backgrounds; and (2) to obtain specific accuracy for each of the four START categories: red, yellow, green, and black. Methods: A systematic review and meta-analysis was conducted that searched Medline (OVID), Embase (OVID), Global Health (OVID), CINAHL (EBSCO), Compendex (Engineering Village), SCOPUS, ProQuest Dissertations and Theses Global, Cochrane Library, and PROSPERO. The results were expanded by hand searching of journals, reference lists, and the grey literature. The search was executed in March 2020. The review considered the participants, interventions, context, and outcome (PICO) framework and followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Accuracy outcomes are presented as means with 95% confidence intervals (CI) as calculated using the binomial method. Pooled meta-analyses of accuracy outcomes using fixed and random effects models were calculated and the heterogeneity was assessed using the Q statistic. Results: Thirty-two studies were included in the review, most of which utilized a non-randomized study design (84%). Proportion of victims correctly triaged using START ranged from 0.27 to 0.99 with an overall triage accuracy of 0.73 (95% CI, 0.67 to 0.78). Proportion of over-triage was 0.14 (95% CI, 0.11 to 0.17) while the proportion of under-triage was 0.10 (95% CI, 0.072 to 0.14). There was significant heterogeneity of the studies for all outcomes (P <.0001). Conclusion: This meta-analysis suggests that START is not accurate enough to serve as a reliable disaster triage tool. Although the accuracy of START may be similar to other models of disaster triage, development of a more accurate triage method should be urgently pursued. [ABSTRACT FROM AUTHOR]

We recognise that the death of a child is one of the most difficult events a family can experience. The article is based on a tragedy that occurred last year near Gunung Lang in Perak, Malaysia, in which a mother was the only survivor of a road accident in which she lost three young sons. It was reported that the husband, who was not involved in the crash, wanted to tell her about the loss. This raises two important moral questions: first, who would be the best person to break the news of the children's death; second, what would be the healthcare professional's ethical responsibility in advocating for and supporting the best interests of the bereaved parents? The case illustrates the need for further ethical reflection by healthcare professionals while supporting bereaved parents. Healthcare professionals must also be sensitive to their ethical responsibility to promote the best interests of grieving parents, which reflects higher vulnerability for bereaved parents. [ABSTRACT FROM AUTHOR]

Copyright of Sultan Qaboos University Journal for Science is the property of Sultan Qaboos University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

After every major process safety incident, one question that invariably surfaces is "Why do we keep having incidents?" In this article and presentation, nine "I" factors will be presented as potential answers to this question. These factors, (which include inexperience, intentionality, and impermanence, among others) will also be analyzed for potential answers to the related question, "What can we do to break the incident cycle?" [ABSTRACT FROM AUTHOR]

Abstract Background Task shifting and task sharing in health care are rapidly becoming more common as the shortage of physicians increases. However, research has not yet examined the changing roles of hospital administrative staff. This study clarified: (1) the adverse incidents caused by hospital administrative staff, and the direct and indirect impact of these incidents on patient care; and (2) the incidents that directly involved hospital administrative staff. Methods This study used case report data from the Japan Council for Quality Health care collected from April 1, 2010 to March 31, 2019, including a total of 30,823 reports. In April 2020, only the 88 self-reported incidents by hospital administrative staff were downloaded, excluding incidents reported by those in medical and co-medical occupations. Data from three reports implicating pharmacists were rejected and the quantitative and textual data from the remaining 85 case reports were analyzed in terms of whether they impacted patient care directly or indirectly. Results Thirty-nine reports (45.9%) involved direct impact on patient care, while 46 (54.1%) involved indirect impact on patient care. Most incidents that directly impacted patient care involved administrative staff writing prescriptions on behalf of a doctor (n = 24, 61.5%); followed by errors related to system administration, information, and documentation (n = 7, 17.9%). Most reported errors that indirectly affected patient care were related to system administration, information, and documentation used by administrative staff (n = 22, 47.8%), or to reception (n = 9, 19.6%). Almost all errors occurred during weekdays. Most frequent incidents involved outpatients (n = 23, 27.1%), or occurred next to examination/operation rooms (n = 12, 14.1%). Further, a total of 14 cases (16.5%) involved patient misidentification. Conclusions Incidents involving hospital administrative staff, the most common of which are medication errors from incorrect prescriptions, can lead to severe consequences for patients. Given that administrative staff now form a part of medical treatment teams, improvements in patient care may require further submission and review of incident reports involving administrative staff.

Despite rapid advances in automated text processing, many related tasks in transit and other transportation agencies are still performed manually. For example, incident management reports are often manually processed and subsequently stored in a standardized format for later use. The information contained in such reports can be valuable for many reasons: identification of issues with response actions, underlying causes of each incident, impacts on the system, etc. In this article, we develop a comprehensive, pragmatic automated framework for analyzing rail incident reports to support a wide range of applications and functions, depending on the constraints of the available data. The objectives are twofold: a) extract information that is required in the standard report forms (automation), and b) extract other useful content and insights from the unstructured text in the original report that would have otherwise been lost/ignored (knowledge discovery). The approach is demonstrated through a case study involving analysis of 23,728 records of general incidents in the London Underground (LU). The results show that it is possible to automatically extract delays, impacts on trains, mitigating strategies, underlying incident causes, and insights related to the potential actions and causes, as well as accurate classification of incidents into predefined categories.

Adequate comprehension of the genomics of microbial resistance to an antimicrobial agent will advance knowledge on the management of associated pathologies and public health safety. However, continued emergences and reemergence of pathogens, including Vibrio species, hallmarks a potential knowledge gap. A clear understanding of the process and forecast of the next trend should be in place to nip in the bud, microbial acquisition of resistance to antibiotics. Therefore, this two-decade (1 January 2000 to 31 December 2019) systematic review and meta-analytical study articulated the prevalence and incidence of antibiotics resistance genes in Vibrio species isolated from environmental samples. Articles from the Web of Science and PubMed electronic databases was engaged. Heterogeneity of the data and bias were analyzed with random effect model meta-analysis and funnel plot. A total of 1920 Vibrio sp. were reported by the ten selected articles included in this study; out of which 32.39% of identified isolates displayed antimicrobial resistance and associated genes. The distribution of antibiotics resistance genes in Vibrio sp., reported within six countries was 21% tetracycline (tet), and 20% sulphonamide (sul) and β-lactamase (bla) respectively. The quinolone, tetracycline and sulfonamide resistance genes showed 32.97% (95% CI 0.18–0.53) prevalence while chloramphenicol, macrolides and aminoglycoside resistance genes are expressed in percentages as 28.67% (95% CI 0.15–0.47) and β-lactamase resistance genes 27.93% (95% CI 0.11–0.56) respectively. The Vibrio antibiotics resistance genes (V-ARG) distribution depicts no regular trend or pattern from the analyzed data. Consequently, more studies would be required to articulate the structure of cohesion in the distribution of the resistance determinants in microbes.

Background: Medication safety in residential care facilities is a critical concern, particularly when nonmedical staff provide medication assistance. The complex nature of medication-related incidents in these settings, coupled with the psychological impact on health care providers, underscores the need for effective incident analysis and preventive strategies. A thorough understanding of the root causes, typically through incident-report analysis, is essential for mitigating medication-related incidents., Objective: We aimed to develop and evaluate a multilabel classifier using natural language processing to identify factors contributing to medication-related incidents using incident report descriptions from residential care facilities, with a focus on incidents involving nonmedical staff., Methods: We analyzed 2143 incident reports, comprising 7121 sentences, from residential care facilities in Japan between April 1, 2015, and March 31, 2016. The incident factors were annotated using sentences based on an established organizational factor model and previous research findings. The following 9 factors were defined: procedure adherence, medicine, resident, resident family, nonmedical staff, medical staff, team, environment, and organizational management. To assess the label criteria, 2 researchers with relevant medical knowledge annotated a subset of 50 reports; the interannotator agreement was measured using Cohen κ. The entire data set was subsequently annotated by 1 researcher. Multiple labels were assigned to each sentence. A multilabel classifier was developed using deep learning models, including 2 Bidirectional Encoder Representations From Transformers (BERT)-type models (Tohoku-BERT and a University of Tokyo Hospital BERT pretrained with Japanese clinical text: UTH-BERT) and an Efficiently Learning Encoder That Classifies Token Replacements Accurately (ELECTRA), pretrained on Japanese text. Both sentence- and report-level training were performed; the performance was evaluated by the F 1 -score and exact match accuracy through 5-fold cross-validation., Results: Among all 7121 sentences, 1167, 694, 2455, 23, 1905, 46, 195, 1104, and 195 included "procedure adherence," "medicine," "resident," "resident family," "nonmedical staff," "medical staff," "team," "environment," and "organizational management," respectively. Owing to limited labels, "resident family" and "medical staff" were omitted from the model development process. The interannotator agreement values were higher than 0.6 for each label. A total of 10, 278, and 1855 reports contained no, 1, and multiple labels, respectively. The models trained using the report data outperformed those trained using sentences, with macro F 1 -scores of 0.744, 0.675, and 0.735 for Tohoku-BERT, UTH-BERT, and ELECTRA, respectively. The report-trained models also demonstrated better exact match accuracy, with 0.411, 0.389, and 0.399 for Tohoku-BERT, UTH-BERT, and ELECTRA, respectively. Notably, the accuracy was consistent even when the analysis was confined to reports containing multiple labels., Conclusions: The multilabel classifier developed in our study demonstrated potential for identifying various factors associated with medication-related incidents using incident reports from residential care facilities. Thus, this classifier can facilitate prompt analysis of incident factors, thereby contributing to risk management and the development of preventive strategies., (©Hayato Kizaki, Hiroki Satoh, Sayaka Ebara, Satoshi Watabe, Yasufumi Sawada, Shungo Imai, Satoko Hori. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 23.07.2024.)

Wildlife rescue centres are specialised units with ecological, conservational and veterinary medical activities, which include treatment, rehabilitation, breeding and releasing rare and endangered wild animals into their natural habitat, as well as environmental education. These centres provide an opportunity to monitor ongoing ecological changes in wildlife, environmental pollution and emerging diseases. With the present study, we aimed to analyse the causes and conservation status of the largest wildlife rehabilitation centre in Bulgaria. A total of 18,720 patients, from 26 orders with various conservation statuses and different etiology, have been admitted to the rehabilitation centre for over 25 years. The summarised results showed that 40% of the patients were admitted with an unknown etiology and the proportion of anthropogenic causes was 18%. Natural factors related to incidents with wild animals were 32%, while a share of 10% of the wildlife which resided at the WRBC referred to a part of re-introduction programmes. This type of analysis of wildlife rehabilitation centres could provide useful information about the status of populations and ecosystems, as well as support conservation practices., Competing Interests: No conflict of interest to declare Disclaimer: This article is (co-)authored by any of the Editors-in-Chief, Managing Editors or their deputies in this journal., (Ivanka Asenova Lazarova, Gergana Nikolova Balieva, Ivailo Klisurov.)

Copyright of Revista Ciência Agronômica is the property of Revista Ciencia Agronomica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Introduction: Patient safety constitutes a priority in health care, being the surgical safety checklist one of the strategies implemented by the WHO. The objective was to determine the level of compliance in the application of the surgical safety checklist in surgical room personnel of a public institution. Materials and Methods: Cross-sectional study in 45 members of the surgical team in a hospital, in which compliance to the WHO checklist was evaluated during July and August 2018. Results: Overall compliance was 13.3% (n=6), with the pre-anesthesia phase reaching the highest level (55.6%, n=25). The highest compliance was recorded by the surgical instrumentation staff (100%, n=8), while the lowest by the nursing staff (25%, n=3), with statistically significant differences (p=0.005). Additionally, a correlation was observed between years of work in the service and compliance in the transfer phase (rho= -0.30, p=0.048). Discussion: Overall compliance was low, which corroborates the stated hypothesis and is similar to other studies described in the literature. Conclusions: The overall compliance to the checklist was very low, with differential behaviors as the surgical instrument technicians showed the highest compliance and the nursing staff the lowest. The antibiotic prophylaxis item was the least compliant, while the pre-anesthesia phase was the most compliant. [ABSTRACT FROM AUTHOR]

ABSTRACT Until recently, accident indicators were analysed separately due to the methods employed, however, the joint use of neural networks and clustering techniques has proven to be an excellent tool for analysing how accidents occur. As such, the aim of this study was to use neural networks and cluster analysis on accident indicators involving tractors on federal highways in the south-east of Brazil. A total of 496 incidents were analysed between 2007 and 2016. The indicators for the accidents under evaluation were time, type of accident, cause of accident, weather conditions, condition of the victims, road layout and federated state. The use of neural networks was based on self-organising maps (SOM), hierarchical clustering employing dendrograms, and non-hierarchical clustering employing the k-means coefficient. Using these techniques, it was possible to divide the incidents into 18 accident groups, of which 11 were represented by the state of Minas Gerais, one group where casualties were predominant, and one group with fatalities. It proved possible to analyse the factors that led to the accident, together with its consequences. Machine traffic during periods of low natural light on straight roads caused rear-end collisions, with casualties and fatalities