Common Phrases Research Articles

Identification of Factors Leading to Unfit Medical Products and Other Health Commodities, Disposal Management and Related Perceived Consequences to Health Supply Chain in Rwanda. Case of Rwanda Medical Supply Ltd and Medical & Allied Service Solutions Ltd

Abstract Background Unfit medical products, especially poor quality / damaged / expired / and phased out (no longer accepted for use) are among health challenges that need careful attention and adequate management in developing countries health supply chains, including Rwanda. Not only does medical wastage affect the public health in general, but also it affects financial capabilities. Actually, according to the Management Science for Health, countries on average spend about 25% of their total health expenditure on medicines, and a significant part of the funds expended on critical drugs is wasted. Objectives The main objective of this study is to identify the factors leading to unfit medical products and related perceived consequences to the supply chain management in Rwanda. Specific Objectives I. To identify the most commonly found unfit medical products in RMS and MEDIASOL. II. Explore factors contributing to unfit medico-pharmaceuticals in RMS and MEDIASOL. III. To explore perceived consequences of unfit medical products to the supply chain management in RMS and MEDIASOL. IV. To explore the disposal methods of unfit pharmaceuticals management in RMS and MEDIASOL Methods The mixed method research design used, data were collected from 20 key informant by doing interview, A questionnaire containing open questions was used and cross-sectional study were used both focusing on identify the most commonly found unfit medical products in RMS and their management, then the shared data were transcribed and translated by the researchers. Main ideas from each Key informant were merged under each theme and sub themes by reading and then rereading to identify common words, phrases and perceptions that were coded. Quantitative data were coded and analyzed using SPSS and EXCEL software. Results The study found that the most common unfit medico-pharmaceuticals are the laboratory commodities, malaria commodities and program products including quinine, and Haloperidol retard 50mg/ml injection and Insulin combinations, the study indicated that the Lack of local manufacturers of pharmaceutical products according to market needs; Change of medical products protocols; Minimum shelf life not specified while ordering the products; and Lack of accurate data to facilitate quantification were highlighted by the respondents as the main factors leading to unfit medicinal products. And the study found that the total unfit products were 3% of the average annual loss in unfit medicinal products compared to the total stock. The study recommended that further research should be done on this topic especially by exploring effective mechanisms and strategies to mitigate the wastage of medical products and other health commodities in Rwanda. Conclusions Medical products are critical for preserving lives in morbid diseases, yet they can get unfit from their intended use for different identified reasons in this study as presented in the summary of factors leading to unfit medical products. Those factors are for example the: Low consumption rate of some products; Sudden change of medical treatment protocols; Procurements without specific minimum required shelf life of products at arrival; etc. Then, were identified twenty-three (23) common unfit products also presented in the results, as well as the perceived consequences of unfit medical products to the supply chain; and three main applied disposal methods are incineration, plastic materials recycling and reuse of unfit products for other purposes. Future studies are needed to explore this important topic more broadly and come up with sustainable strategies to properly manage the medical products throughout the entire supply chain

Proficiency of Turing test on Nigerian Major Languages; Yoruba, Igbo and Hausa Using Google Translate

Google Translate performance evaluation in translating between English and three Nigerian languages: Yoruba, Igbo, and Hausa, highlighting its strengths and limitations using the Turing Test. The study assessed whether participants could distinguish Artificial Intelligence (AI) - generated translations from human translations which reveals that Google Translate handles basic communication in these languages but struggles with more complex linguistic features. The tool adequately translates straightforward sentences in Yoruba language, but falters with idiomatic expressions, proverbs, and tonal nuances, the same problem refects in Igbo language facing challenges with more complex sentences and dialectal variations, resulting in culturally irrelevant outputs. Hausa performed better due to the availability of a larger training dataset, delivering more accurate translations for common phrases, but still struggled with cultural and contextual nuances.The findings suggest that Google Translate's limitations prevent it from passing a Turing Test with native speakers of Yoruba, Igbo, and Hausa. While understandable translations are produced for basic sentences, native speakers can easily detect inconsistencies, awkward phrasing, and a lack of cultural nuance. These issues underscore the significant gap between AI translation and human-like proficiency for African languages. Keyword: Turing test, Nigerian Major Languages; Yoruba, Igbo, Hausa Using Google Translate Abiola, O.A., Adebisi R.O. & Akinola, S.O. (2024): Proficiency of Turing test on Nigerian Major Languages; Yoruba, Igbo and Hausa Using Google Translate. Journal of Advances in Mathematical & Computational Science. Vol. 12, No. 3. Pp 1-12 Available online at www.isteams.net/mathematics-computationaljournal. dx.doi.org/10.22624/AIMS/MATHS/V12N4P1

Application Of the National Corpus Of The Kazakh Language In Linguistic Research

National corpora are essential tools in modern linguistics, offering valuable data for analyzing language structures and their connection to the world.The primary goal of this study is to enhance the perception and analysis of the Kazakh language. It introduces a methodological approach that deepens the understanding of the structure and features of Kazakh.The research utilizes frequency analysis, syntactic parsing, and statistical methods to examine Kazakh language texts. The results demonstrate that the National Corpus provides extensive data for studying linguistic patterns. Frequency analysis identifies the most common words and phrases, while syntactic analysis uncovers patterns and connections between words. The study also highlights the variation in word usage and constructions, showing the language's adaptability to modern communication.The findings are applicable to lexicography, semantics, and syntax studies of the Kazakh language. Additionally, they offer practical benefits in language teaching, lexicographic resource creation, machine translation, and natural language processing.In conclusion, the research provides valuable tools for in-depth analysis of the Kazakh language, with broad applications across linguistic studies and practical fields.

Comparative Analysis of Online Translators in the Machine Translation System

The article presents a comparative analysis of various online translators in the machine translation system. The author of the study focused on finding out the effectiveness and accuracy of the translation, identifying the strengths and weaknesses of the three most common online translators: Google Translate, DeepL and Microsoft Translator. The article begins with an overview of current trends in the field of machine translation and the need for online translations based on this context. The research methodology is selected in the article, in particular, the selection of individual online translators for comparison and the criteria for evaluating their work. The researcher analyzes the quality of translations for various types of texts, including common phrases, technical terms, complex sentences, etc. Attention is also paid to different language pairs and ensuring the availability of rarer languages in electronic translation. The article reveals such aspects as accuracy of translation, speed of work, recognition of context and idiomatic expressions, availability of additional functions and capabilities that ensure better translation quality. The author thoroughly highlights the strengths and weaknesses of each system, provides clear information about the use of integrations, available functions and additional capabilities of each translator. In the final part of the article, the author provides conclusions and recommendations regarding the use of online translators in the machine translation system. The research highlights that each electronic translator has its own advantages and limitations, and the choice depends on the specific needs of the user. In addition, the author emphasizes the need for constant updating and improvement of online translators to increase their accuracy and efficiency. This comparative analysis will help simplify the selection of the optimal translation software for users who require high-quality machine translation for their professional activities or everyday needs.

Disease Evolution-based Specificity Target Discovery (DESTD) by analyzing Jiawei-Maxing-Shigan Decoctions against COVID-19

IntroductionDiverse disease courses, each corresponding to a unique pathophysiological mechanism, characterize the coronavirus 2019 disease (COVID-19). Intensive research on discovering specificity targets for treating COVID-19 has significant implications for understanding pathogenesis mechanisms in disease evolution. Traditional Chinese medicine (TCM) has shown distinctive advantages in treating COVID-19 in different periods, which may provide new clues for target discovery. In order to accomplish the goal of providing a Disease Evolution-based Specificity Target Discovery (DESTD), the purpose of this paper was to conduct an analysis of the difference in target weighted value during each cycle of COVID-19. MethodsOur study employed a comprehensive methodology combining TCM pharmacology with modern biological techniques. Three Jiawei-Maxing-Shigan Decoctions (MS Decoction), were selected as samples, including Hanshi-Yufei decoction (HY Decoction), Shidu-Yufei decoction (SY Decoction), and Yidu-Bifei decoction (YB Decoction). The core components of these decoctions are the same, while the differences correspond to different periods of COVID-19. The DESTD approach consists of several parts: network pharmacology analysis to identify potential targets, molecular docking simulation to investigate TCM components acting on potential targets, and biological validation. This study employed several cell line models, including RAW264.7 (Mouse Mononuclear Macrophages Cells), 16HBE (Human Bronchial Epithelial Cells), and HUVECC (Human Umbilical Vein Endothelial Cells), to explore new approaches for key target discovery. ResultsDuring the mild phase of COVID-19, HY Decoction exhibited anti-inflammatory effects by specifically down-regulating the expression of heat shock protein HSP 90-alpha (HSP90AA1), transcription factor JUN (JUN) and other five genes in RAW264.7. In the common phrase, inflammation and fibrosis could be specifically alleviated by down-regulating the expression of interleukin-10 (IL10) and mitogen-activated protein kinase 14 (MAPK14) by SY Decoction respectively acting on RAW264.7 and 16HBE cell lines. In severe cases, YB Decoction specifically down-regulated the expression of prostaglandin endoperoxide synthase-2 (PTGS2), caspase-3 (CASP3), and three other genes to protect vascular endothelial cells from oxidative damage. Moreover, YB also played a role in anti-fibrosis by specifically down-regulating the expression of estrogen receptor beta (ESR2), vascular cell adhesion protein 1 (VCAM1), and other three genes, and up-regulating the expression of prostaglandin endoperoxide synthase-1 (PTGS1) in the severe period. These targeted actions align with a holistic treatment approach and provide tailored therapies for different stages of COVID-19. ConclusionThis research proposed a novel strategy for Disease Evolution-based Specificity Target Discovery (DESTD) to identify specific targets of different stages of disease and specific targets and mechanisms throughout the evolution of COVID-19.

Israel’s war on journalism: A Kiwi journalist’s response

Whether it is termed as ‘self-defence’ or ‘mowing the lawn’, the Israel Defence Force (IDF) has common phrases to describe its ongoing attacks on the besieged Palestine enclave of Gaza. The phrases obscure the devastation of death and disaster in the 2023/24 genocidal war on Gaza. While global reports have tended to focus on the horrendous and rapid climb of civilian casualties, especially women and children, Gaza has also claimed the worst ever death rate of journalists, many apparently targeted by the IDF because of their profession. Journalists in this article argue that it is time to ‘call these [Israeli state] terrorists by their true name: enablers of genocide.’

Automatic text classification of drug-induced liver injury using document-term matrix and XGBoost.

Regulatory agencies generate a vast amount of textual data in the review process. For example, drug labeling serves as a valuable resource for regulatory agencies, such as U.S. Food and Drug Administration (FDA) and Europe Medical Agency (EMA), to communicate drug safety and effectiveness information to healthcare professionals and patients. Drug labeling also serves as a resource for pharmacovigilance and drug safety research. Automated text classification would significantly improve the analysis of drug labeling documents and conserve reviewer resources. We utilized artificial intelligence in this study to classify drug-induced liver injury (DILI)-related content from drug labeling documents based on FDA's DILIrank dataset. We employed text mining and XGBoost models and utilized the Preferred Terms of Medical queries for adverse event standards to simplify the elimination of common words and phrases while retaining medical standard terms for FDA and EMA drug label datasets. Then, we constructed a document term matrix using weights computed by Term Frequency-Inverse Document Frequency (TF-IDF) for each included word/term/token. The automatic text classification model exhibited robust performance in predicting DILI, achieving cross-validation AUC scores exceeding 0.90 for both drug labels from FDA and EMA and literature abstracts from the Critical Assessment of Massive Data Analysis (CAMDA). Moreover, the text mining and XGBoost functions demonstrated in this study can be applied to other text processing and classification tasks.

Anesthesia as a Team Sport: Common Catch Phrase or Essential Component of Patient Care?

Anesthesia as a Team Sport: Common Catch Phrase or Essential Component of Patient Care?

Telling Time: Patient Experiences of Temporality in Brain Tumor Comics.

This article explores three different comics by creators with brain tumors: Rick, written and drawn by Gordon Shaw; Going Remote, written by Adam Bessie and drawn by Peter Glanting; and Parenthesis, written and drawn by Élodie Durand. It examines how the affordances of the comics medium enables the creators to present an experience of subjective time that is multiple, diffuse, and contradictory, in contrast to the regular apportioning of time via calendars, schedules, and pathways essential to institutional neuro-oncology. The question of time here is significant because the side effects of brain tumors can include blackouts, seizures, and periods of extreme fatigue, during which the experience of time can be significantly disrupted. The title of the article therefore evokes a temporal duality: on the one hand, it refers to the common phrase used to describe what clocks do, as well as our ability to read them; on the other hand, it speaks to one of the most important qualities of graphic medicine, which is that it allows patients dealing with medical or health issues to tell time differently. The article explores the representation of personal time in Rick, social time in Going Remote, and lost time in Parenthesis.

“Inside jokes and the funny things”: Belongingness in College Students’ Rhetorical Uses of Venmo

Within the growing use of peer-to-peer mobile payment apps, Venmo has proven most popular among the college-aged population in the United States. “Venmo me” is a common phrase overheard among students on college campuses. Spurred by an exchange in a course focused on social media, we investigate the uses of Venmo among students on our campus. Through interviews, we examine students’ relationships to not only the transactional but also the rhetorical aspects of Venmo, especially the social feed (unique among peer-to-peer mobile payment apps). We reflect on three recurring themes in these interviews using the concept of public displays of belongingness: insider versus outsider, requesting payment, and digital intimacy.

DEVELOPMENT OF MOBILE APPLICATION FOR SIGN LANGUAGE TRANSLATION USING DEEP LEARNING TECHNIQUE

Sign Language is a visual and gestural language used by deaf and hard-of-hearing people to communicate. However, communication between hearing and non-hearing individuals can be challenging due to the language barrier. In order to overcome this barrier, a Mobile based Translator is being proposed using Convolutional Neural Network (CNN) to recognize and translate hand gestures in real-time. The proposed system consists of a CNN model trained on a large dataset of hand gestures that includes various signs, such as the alphabet, numbers, and common phrases to recognize various signs, and a backend server to handle the translation of the recognized signs into text. The system was implemented using various CNN architectures like ResNet, MobileNet and VGG-16, where the later gave the best accuracy of 97.69%. The trained VGG-16 model recognizes the signs by extracting features from the images of the hand gestures and using these features to classify the gestures. Once a gesture is recognized, the backend server translates it into text using a pre-defined mapping of signs to words or phrases. The translated text will then be displayed to the user in real-time on the mobile app, enabling seamless communication between hearing and non-hearing individuals. The proposed system was implemented as a mobile app using Flutter, which is a cross-platform development framework. This mobile app make communication easier for vulnerable and enable sharing of information without any discrimination.

Application of sentiment and word frequency analysis of physician review sites to evaluate refractive surgery care

BackgroundOnline physician reviews increase transparency in health care, helping patients make informed decisions about their provider. Language processing techniques can quantify this data and allow providers to better understand patients' experiences, perspectives, and priorities. The objective of this study was to assess patient satisfaction and understand the aspects of care that are valued by patients seeking refractive care using sentiment and word frequency analysis. MethodsWritten reviews and Star ratings for members of the Refractive Surgery Alliance Society practicing in the United States were collected from Healthgrades, a popular physician rating website. Surgeons with at least one written review were included in the study. Reviews were scored from -1 (most negative) to +1 (most positive) using Valence Aware Dictionary sEntiment Reasoner (VADER). Reviews were stratified by demographic characteristics, namely gender, region, and years in practice. Word frequency analysis was applied to find the most common words and phrases. ResultsA total of 254 specialists and 3104 reviews were analyzed, with an average of 4.4/5 stars and mean 48 ratings each. Most physicians had positive reviews (96%, average VADER ​= ​0.69). Younger physicians (<20 years since residency) had significantly higher Stars rating than senior peers (>20 years) (P ​< ​0.001). A similar trend was observed in VADER score (0.71 vs 0.69), although not statistically significant (P ​= ​0.06). No statistical differences were observed between Stars rating and VADER score by gender (P ​= ​0.66, P ​= ​0.83) or by geographical region (P ​= ​0.74, P ​= ​0.07). "Staff" (n ​= ​1269), "professional" (n ​= ​631), "office" (n ​= ​523), "questions" (n ​= ​424), and "friendly" (n ​= ​386) were frequently used in reviews, along with phrases such as "the staff" (n ​= ​273) and "my questions" (n ​= ​174). "Surgery" (n ​= ​719), "staff" (n ​= ​576), "procedure" (n ​= ​251), "experience" (n ​= ​243), and "professional" (n ​= ​240) were the most common words in positive reviews, while "surgery" (n ​= ​147), "office" (n ​= ​86), "staff" (n ​= ​54), "time" (n ​= ​47), and "insurance" (n ​= ​28) were the most commonly used in negative reviews. ConclusionsBoth the average Stars and VADER sentiment score suggest a high satisfaction among refractive patients. Word frequency analysis revealed that patients value non-clinical aspects of care, including interactions with staff, insurance coverage, and wait-times, suggesting that improving non-clinical factors could enhance patient satisfaction with refractive surgery.

‘My doctor just called me a good girl and I died a bit inside’: From everyday misogyny to obstetric violence in UK fertility and maternity services

This paper begins with the common phrase ‘good girl’ as a lens through which to explore the insidious nature of patronising and paternalistic language on women's agency in obstetric care. Here we see how misogynistic language is both violence against women in its own right, and serves to create a context in which more extreme obstetric violence can be precipitated. Based on thematic analysis of discussion on Mumsnet, and on contributions to a research-focused Facebook group, this paper illustrates the complexity of recognising and refuting misogyny as a female patient as well as the damage that can occur from a cultural context in which this language is normalised. Here, words both boast a materiality through the environments they reify, and become transient and slippery, with semiotic uncertainty.

Lessons learned from annotation of VAERS reports on adverse events following influenza vaccination and related to Guillain-Barré syndrome

BackgroundVaccine Adverse Events ReportingSystem (VAERS) is a promising resource of tracking adverse events following immunization. Medical Dictionary for Regulatory Activities (MedDRA) terminology used for coding adverse events in VAERS reports has several limitations. We focus on developing an automated system for semantic extraction of adverse events following vaccination and their temporal relationships for a better understanding of VAERS data and its integration into other applications. The aim of the present studyis to summarize the lessons learned during the initial phase of this project in annotating adverse events following influenza vaccination and related to Guillain-Barré syndrome (GBS). We emphasize on identifying the limitations of VAERS and MedDRA.ResultsWe collected 282 VAERS reports documented between 1990 and 2016 and shortlisted those with at least 1,100 characters in the report. We used a subset of 50 reports for the preliminary investigation and annotated all adverse events following influenza vaccination by mapping to representative MedDRA terms. Associated time expressions were annotated when available. We used 16 System Organ Class (SOC) level MedDRA terms to map GBS related adverse events and expanded some SOC terms to Lowest Level Terms (LLT) for granular representation. We annotated three broad categories of events such as problems, clinical investigations, and treatments/procedures. The inter-annotator agreement of events achieved was 86%. Incomplete reports, typographical errors, lack of clarity and coherence, repeated texts, unavailability of associated temporal information, difficulty to interpret due to incorrect grammar, use of generalized terms to describe adverse events / symptoms, uncommon abbreviations, difficulty annotating multiple events with a conjunction / common phrase, irrelevant historical events and coexisting events were some of the challenges encountered. Some of the limitations we noted are in agreement with previous reports.ConclusionsWe reported the challenges encountered and lessons learned during annotation of adverse events in VAERS reports following influenza vaccination and related to GBS. Though the challenges may be due to the inevitable limitations of public reporting systems and widely reported limitations of MedDRA, we emphasize the need to understand these limitations and extraction of other supportive information for a better understanding of adverse events following vaccination.

Mining Text Value in Japanese Translation Teaching Based on KH Coder Text Data

Abstract Vocabulary serves as the cornerstone of Japanese translation instruction, with the identification of high-frequency terms within educational texts yielding significant pedagogical insights. This study employs the KH-Coder software to extract feature words frequently used in Japanese translation teaching materials. Through a combination of covariate network analysis, sentiment analysis, and hierarchical clustering, the paper delves into the intrinsic textual value of these educational resources. Furthermore, it integrates both generative and discriminative approaches to conduct dependency parsing of Japanese translation. By incorporating probabilistic functions, the methodology enhances contextual understanding, ensuring that each annotation unit within a sentence is optimally tagged. Analysis of the CGTT-35 corpus through KH-Coder reveals a list of the top 50 high-frequency words in Japanese translation teaching, each appearing over 100 times. This enables students to use these frequent words and sentence structures as cues to master common phrases and familiarize themselves with core content. The approach has proven effective in enhancing students’ receptivity to Japanese translation instruction and their comprehension capabilities, offering valuable implications for teaching strategies.

Semantic Similarity of Common Verbal Expressions in Older Adults through a Pre-Trained Model

Health problems in older adults lead to situations where communication with peers, family and caregivers becomes challenging for seniors; therefore, it is necessary to use alternative methods to facilitate communication. In this context, Augmentative and Alternative Communication (AAC) methods are widely used to support this population segment. Moreover, with Artificial Intelligence (AI), and specifically, machine learning algorithms, AAC can be improved. Although there have been several studies in this field, it is interesting to analyze common phrases used by seniors, depending on their context (i.e., slang and everyday expressions typical of their age). This paper proposes a semantic analysis of the common phrases of older adults and their corresponding meanings through Natural Language Processing (NLP) techniques and a pre-trained language model using semantic textual similarity to represent the older adults’ phrases with their corresponding graphic images (pictograms). The results show good scores achieved in the semantic similarity between the phrases of the older adults and the definitions, so the relationship between the phrase and the pictogram has a high degree of probability.

Lyricist as Analyst: Rhyme Scheme as Music-Setting in the Great American Songbook

Abstract Although most songwriting teams in the Great American Songbook wrote music first and lyrics second, most studies of music-text interaction in this repertoire still evince a lyrics-first mindset, in which the music is viewed as text-setting. In this article, I propose the opposite approach: considering lyrics as a form of music-setting, in which the lyricist’s superimposition of a verbal form (the rhyme scheme) upon the composer’s pre-existing musical form counts as an act of analysis. I examine in turn: (1) the most common phrase structures and their associated rhyme schemes in songs from 1919 to 1943; (2) Lorenz Hart’s negotiation of the unusual XYYZ phrase structure that Richard Rodgers frequently employed; and (3) special cases where the lyricist contests the composer’s form and the effect that this can have on performers’ choices.

Patterns of co-first and co-last authorship in cancer phase 3 trials.

50 Background: Phase 3 randomized clinical trials (RCTs) are the gold standard for efficacy in cancer treatment, and the resulting manuscripts carry great weight for clinical practice and for the authors themselves. First/last authorship on high-impact publications is a frequently used metric for promotion and identification of leadership of subsequent studies. Co-first/last (joint) authorship has become more common in many fields; the patterns of joint authorship in cancer phase 3 RCT publications are relatively unexamined. Methods: We identified all phase 3 RCTs published between 1998-2023 in the HemOnc knowledge base as of April 25, 2023. Joint authorship was identified through a combination of parsing PubMed structured data and scraping publisher websites for common phrases e.g., “contributed equally”. The identified manuscripts were matched by year and reference type (primary efficacy reports vs all others) 1:1 to those without joint authorship. Results: Out of N=3911 manuscripts meeting criteria, we identified 382 (10%) with joint authorship (Table). The incidence of joint authorship increased from 1/98 manuscripts published in 1998 to 34/194 (17.5%) in 2022. Manuscripts with joint authorship had a median (range) of 3 (3-13) co-first/last authors, with 54 (14%) having both co-first and co-last authors. Jointly-authored manuscripts had a median (IQR) of 21 authors (17-29) vs 20 (15-23) for matched manuscripts, p&lt;0.001. Of 970 unique authors who had ³1 joint authorship, the most common country of affiliation was China (254, 26%) followed by US (195, 20%); in the matched manuscripts, 243/672 (36%) first/last authors were based in the US, followed by France (65, 10%). Joint authors with known gender were more likely to be women vs matched non-joint first/last authors (OR 1.28, 95% CI 0.99-1.66). Joint authors in an ordinal position other than first or last had a less extensive publication record as of 2023, median (IQR) of 5 (2-11) phase 3 publications vs 4 (2-9), p&lt;0.001. Conclusions: Joint authorship in phase 3 cancer RCTs is uncommon but has increased over the past 25 years. Based on the less extensive publication record of non-first/last joint authors, promotion of relatively junior investigators may play a role in joint authorship decisions. Societal factors may also play a role, as evidenced by the discrepancy between US-based joint and non-joint authors. While not statistically significant, a trend towards more women in joint authorship roles is notable. Further examination of the impact of joint authorship on career trajectory and subsequent trial leadership opportunities is warranted. [Table: see text]

Developing a Word Lexicon from Electronic Health Records for Natural Language Processing Analysis of Free-Text Reports for Patients with Venous Thromboembolism.

Patients with gliomas, particularly glioblastomas (GBM) have a higher risk of developing venous thromboembolism (VTE), including both pulmonary embolism (PE) or deep vein thrombosis (DVT), both correlating with overall survival (OS) and potential biological signaling relevance for analysis of the tumor state in conjunction with evolving biomarkers. Artificial intelligence (AI) approaches thar employ VTE as a clinical feature in brain tumor patients is understudied due to the difficulty in analyzing unstructured clinical data in electronic health records (EHR). Data expansion by creating a word lexicon for natural language processing (NLP) of free-text clinical reports will allow exposure of VTE for classification of large-scale data sets, NLP and AI. Patients with a pathologic diagnosis of GBM (2005-2021) were extracted from EHR and screened for the development of VTE based on radiology free-text reports - ultrasound (US) of extremities and Computed Topography-pulmonary angiogram (CT). Language data were collected manually and verified computationally using radiology report text filtration. Common phrases and words employed were collected to generate a lexicon for future computational analysis. Kaplan-Meier survival analyses were generated for VTE in relation OS and progression-free survival (PFS). A total of 163 patients (mean age = 56.1 ± 12.1, 65% male) were included, 48 (29.4%) were screened for VTE following clinical suspicion on history or physical exam, and 15 (9.2%) were found to have a VTE. Screening methods were US 83.3% (40) or CT 13.9% (6), or both 4.6% (2). 28.6% (12) of US and 37.5% (3) of CT resulted in a positive VTE diagnosis. Terms that were only detected in patients with VTE were for US: "complete" 44%, "thrombosis detected" (8.3%), "occlusive" (16%), "partial" (16%), "critical" (25%), "residual" (8.3%) and for CT: "critical" (67%) and "pulmonary emboli" (33%). Words used in free-text reports showed a high degree of inter-reporter variability. The words "partial", "residual", "complete", "critical" or "clotted" when used as an "or" Boolean statement applied to US and CT radiology reports identified ∼93% of the patients with VTE. Patients with VTE had worse OS (median 14 vs. 19 months, p = .0189) and PFS (median 6 vs. 9 months, p = .0239) compared to patients without VTE indicating underlying pathology associated with both prevalence of VTE and tumor burden and the need to incorporate VTE as a clinical feature in large scale data analyses. US and CT yield a similar percentage of positive VTE findings while employing different terms to characterize VTE. We confirm that patients with VTE have poorer outcomes and present a word combination that identifies patients with VTE in large-scale radiology report data. Further research will focus on validation in other datasets, NLP approaches to optimize and data aggregation with laboratory data and large-scale omics panels to expose thrombosis as a clinical feature for biomarker analysis.

The RAAF's Frozen Middle Part Two

The concept of the "frozen middle" has become a common phrase in the business world, and it refers to the group of middle managers who are often resistant to change. These managers are known to be stuck in their ways, resistant to new ideas, and hesitant to take risks. The frozen middle is a significant problem for many organisations such as the Royal Australian Air Force (RAAF), as it hinders innovation, slows down decision-making processes, and stifles progress. To understand how the frozen middle can impact organisational efficiency, it is essential to explore why this phenomenon exists and how it can be addressed. In my previous post I used Maslow’s Hierarchy of Needs as a lens to describe a possible reason why the problem exists (Williamson, 2023). In this post, I will provide an alternative explanation from a cognitive lens.