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International Journal of Systematic and... Jun 2024
Topics: Terminology as Topic; Publishing
PubMed: 38828845
DOI: 10.1099/ijsem.0.006275 -
Zhonghua Bing Li Xue Za Zhi = Chinese... Jun 2024The 5th edition of the World Health Organization (WHO) classification of haematolymphoid tumours used the hierarchical system to classify T-cell and NK-cell lymphoid...
The 5th edition of the World Health Organization (WHO) classification of haematolymphoid tumours used the hierarchical system to classify T-cell and NK-cell lymphoid proliferations and lymphomas (T/NK-LPD/LYM) based on research advances and clinicopathological characteristics of the diseases. In this edition of classification, tumour-like lesions were included, some tumors were added/deleted, the names or terms of certain diseases were refined, and the diagnostic criteria or subtypes of some diseases were revised. This group of diseases was reintegrated from non-clonal hyperplasia to highly aggressive lymphoma, which would further reflect the nature of T/NK-LPD/LYM and benefit to clinical application.
Topics: Humans; World Health Organization; Killer Cells, Natural; T-Lymphocytes; Lymphoma; Lymphoma, T-Cell; Lymphoproliferative Disorders
PubMed: 38825896
DOI: 10.3760/cma.j.cn112151-20230823-00094 -
Appetite May 2024Consuming enough energy to meet high energy demands can be challenging for military personnel wherein logistical constraints limit food availability. Increasing dietary...
Consuming enough energy to meet high energy demands can be challenging for military personnel wherein logistical constraints limit food availability. Increasing dietary energy density (ED) and/or volume density (VD) of rations may be countermeasures, but whether positive linear associations between ED and energy intake (EI) hold at moderate-to-high ED and VD is unclear. This study examined the effects of covertly increasing the ED and VD of moderate ED (≥1.6 kcal/g) foods on appetite and energy intake. Twenty healthy men completed four 2-day treatments in random order by consuming a standardized diet containing three experimental food items (EXP) engineered using leavening, physical compression and fat manipulation to be isovolumetric but lower (L) or higher (H) in ED and VD creating four treatments: LED/LVD, LED/HVD, HED/LVD, HED/HVD. Consumption of EXP was compulsory during two meals and a snack, but remaining intake was self-selected (SSF). Results failed to show any ED-by-VD interactions. During LVD, EI was lower for EXP (-417 kcal [95%CI: 432, -402], p < 0.01) and TOTAL (SSF + EXP) (-276 kcal [95%CI: 470, -83], p = 0.01) compared to HVD, while SSF EI did not differ (140 kcal [-51, 332], p = 0.15). During LED, EI for EXP (-291 kcal [95%CI: 306, -276], p < 0.01) was lower than HED, while SSF EI was higher than HED (203 kcal 95%CI: [12, 394], p = 0.04) and TOTAL EI did not differ (-88 kcal [-282, 105], p = 0.36). Thus, when a small isovolumetric portion of the diet was manipulated, increasing the VD of moderate ED foods failed to elicit compensatory reductions in ad libitum EI while increasing the ED of moderate ED foods did. Findings may support VD manipulation of moderate ED foods as a strategy to promote increased short-term EI in environments wherein logistical burden may limit food volume.
PubMed: 38825013
DOI: 10.1016/j.appet.2024.107537 -
BMC Public Health Jun 2024Globally, the counting of deaths based on gender identity and sexual orientation has been a challenge for health systems. In most cases, non-governmental organizations...
Accuracy, potential, and limitations of probabilistic record linkage in identifying deaths by gender identity and sexual orientation in the state of Rio De Janeiro, Brazil.
BACKGROUND
Globally, the counting of deaths based on gender identity and sexual orientation has been a challenge for health systems. In most cases, non-governmental organizations have dedicated themselves to this work. Despite these efforts in generating information, the scarcity of official data presents significant limitations in policy formulation and actions guided by population needs. Therefore, this manuscript aims to evaluate the accuracy, potential, and limits of probabilistic data relationships to yield information on deaths according to gender identity and sexual orientation in the State of Rio de Janeiro.
METHODS
This study evaluated the accuracy of the probabilistic record linkage to obtain information on deaths according to gender and sexual orientation. Data from two information systems were used from June 15, 2015 to December 31, 2020. We constructed nine probabilistic data relationship strategies and identified the performance and cutoff points of the best strategy.
RESULTS
The best data blocking strategy was established through logical blocks with the first and last names, birthdate, and mother's name in the pairing strategy. With a population base of 80,178 records, 1556 deaths were retrieved. With an area under the curve of 0.979, this strategy presented 93.26% accuracy, 98.46% sensitivity, and 90.04% specificity for the cutoff point ≥ 17.9 of the data relationship score. The adoption of the cutoff point optimized the manual review phase, identifying 2259 (90.04%) of the 2509 false pairs and identifying 1532 (98.46%) of the 1556 true pairs.
CONCLUSION
With the identification of possible strategies for determining probabilistic data relationships, the retrieval of information on mortality according to sexual and gender markers has become feasible. Based on information from the daily routine of health services, the formulation of public policies that consider the LGBTQ + population more closely reflects the reality experienced by these population groups.
Topics: Humans; Brazil; Female; Male; Gender Identity; Sexual Behavior; Medical Record Linkage; Data Accuracy; Death Certificates; Adult
PubMed: 38824562
DOI: 10.1186/s12889-024-19002-x -
Urology May 2024To assess the panel composition of the 2 most important guideline developers in urology as equity and acceptability, important domains in clinical guideline development,...
OBJECTIVE
To assess the panel composition of the 2 most important guideline developers in urology as equity and acceptability, important domains in clinical guideline development, require broad stakeholder representation.
METHODS
Following a predefined protocol, we identified all current AUA and EAU guideline documents. Two authors independently abstracted data including guideline topic, number and roles of panel members, voting status, and academic rank. We determined panel member's gender (woman, man, or nonbinary) and racialization (White or non-White) status based on name, internet picture, pronouns used, bios available, and gender listed on their profile.
RESULTS
We identified 31 AUA and 20 EAU guidelines for inclusion. Median panel size was 19 (interquartile range [IQR]: 17; 21) with 12 (IQR: 10; 14) voting members. The average composition of voting panels was predominantly male (81.8%) and White (86.8%). Eleven guideline panels (21.6%) did not include any women, and 9 (17.6%) panels had no representation of individuals from non-White groups. While gender distribution was similar among guidelines of the 2 organizations, the AUA included more voting members from non-White groups (14.3% vs 8.0%; P = .010). Analysis of the AUA panel composition over time revealed stable proportions of female and non-White individuals.
CONCLUSION
Both AUA and EAU guidelines exhibit insufficient representation of females and non-White individuals, with no evident improvement observed over time. Implementing more transparent processes that advocate for diverse panel representation may enhance the incorporation of stakeholder values and preferences, thereby improving the dissemination and adoption of guidelines.
PubMed: 38823650
DOI: 10.1016/j.urology.2024.05.023 -
Journal of Medical Internet Research May 2024Efficient data exchange and health care interoperability are impeded by medical records often being in nonstandardized or unstructured natural language format. Advanced...
BACKGROUND
Efficient data exchange and health care interoperability are impeded by medical records often being in nonstandardized or unstructured natural language format. Advanced language models, such as large language models (LLMs), may help overcome current challenges in information exchange.
OBJECTIVE
This study aims to evaluate the capability of LLMs in transforming and transferring health care data to support interoperability.
METHODS
Using data from the Medical Information Mart for Intensive Care III and UK Biobank, the study conducted 3 experiments. Experiment 1 assessed the accuracy of transforming structured laboratory results into unstructured format. Experiment 2 explored the conversion of diagnostic codes between the coding frameworks of the ICD-9-CM (International Classification of Diseases, Ninth Revision, Clinical Modification), and Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT) using a traditional mapping table and a text-based approach facilitated by the LLM ChatGPT. Experiment 3 focused on extracting targeted information from unstructured records that included comprehensive clinical information (discharge notes).
RESULTS
The text-based approach showed a high conversion accuracy in transforming laboratory results (experiment 1) and an enhanced consistency in diagnostic code conversion, particularly for frequently used diagnostic names, compared with the traditional mapping approach (experiment 2). In experiment 3, the LLM showed a positive predictive value of 87.2% in extracting generic drug names.
CONCLUSIONS
This study highlighted the potential role of LLMs in significantly improving health care data interoperability, demonstrated by their high accuracy and efficiency in data transformation and exchange. The LLMs hold vast potential for enhancing medical data exchange without complex standardization for medical terms and data structure.
Topics: Humans; Health Information Exchange; Health Information Interoperability; Electronic Health Records; Natural Language Processing; Systematized Nomenclature of Medicine
PubMed: 38819879
DOI: 10.2196/56614 -
Accountability in Research May 2024The Retraction Watch Database (RWDB) is widely used to retrieve retraction data. However, its lack of affiliation normalization hinders the retrieval efficiency of...
The Retraction Watch Database (RWDB) is widely used to retrieve retraction data. However, its lack of affiliation normalization hinders the retrieval efficiency of retraction data for specific research-performing organizations. A query for a university name in the RWDB may yield retraction data entries for other universities with similar names, giving rise to the issue of affiliation naming proximity. This study assessed the impact of this issue on the retrieval efficiency of retraction records for 2,692 Chinese university names in English. The analysis revealed that the retrieval efficiency of retraction records for 206 Chinese university names can be influenced by 408 university names. As of 2022, the retrieval efficiency of retraction records for 96 Chinese university names was compromised by the involvement of 402 university names, resulting in an overall retraction inflation rate of 37.9% and an average rate of 45.0%. The findings highlight the importance of curating retraction data through affiliation-specific queries in the RWDB, adhering to the official English names of Chinese universities for scholarly publishing, and adopting the Research Organization Registry system for affiliation disambiguation. Given the significance of this issue concerning the English names of universities in non-English-speaking countries, the identified causes of the problem and proposed solutions can offer valuable insights for improving the retrieval of retraction records for non-Chinese universities in the RWDB.
PubMed: 38818893
DOI: 10.1080/08989621.2024.2355921 -
Heliyon May 2024Emotion recognition technology through EEG signal analysis is currently a fundamental concept in artificial intelligence. This recognition has major practical... (Review)
Review
Emotion recognition technology through EEG signal analysis is currently a fundamental concept in artificial intelligence. This recognition has major practical implications in emotional health care, human-computer interaction, and so on. This paper provides a comprehensive study of different methods for extracting electroencephalography (EEG) features for emotion recognition from four different perspectives, including time domain features, frequency domain features, time-frequency features, and nonlinear features. We summarize the current pattern recognition methods adopted in most related works, and with the rapid development of deep learning (DL) attracting the attention of researchers in this field, we pay more attention to deep learning-based studies and analyse the characteristics, advantages, disadvantages, and applicable scenarios. Finally, the current challenges and future development directions in this field were summarized. This paper can help novice researchers in this field gain a systematic understanding of the current status of emotion recognition research based on EEG signals and provide ideas for subsequent related research.
PubMed: 38818173
DOI: 10.1016/j.heliyon.2024.e31485 -
Scientific Reports May 2024Post-traumatic stress disorder (PTSD) lacks clear biomarkers in clinical practice. Language as a potential diagnostic biomarker for PTSD is investigated in this study....
Post-traumatic stress disorder (PTSD) lacks clear biomarkers in clinical practice. Language as a potential diagnostic biomarker for PTSD is investigated in this study. We analyze an original cohort of 148 individuals exposed to the November 13, 2015, terrorist attacks in Paris. The interviews, conducted 5-11 months after the event, include individuals from similar socioeconomic backgrounds exposed to the same incident, responding to identical questions and using uniform PTSD measures. Using this dataset to collect nuanced insights that might be clinically relevant, we propose a three-step interdisciplinary methodology that integrates expertise from psychiatry, linguistics, and the Natural Language Processing (NLP) community to examine the relationship between language and PTSD. The first step assesses a clinical psychiatrist's ability to diagnose PTSD using interview transcription alone. The second step uses statistical analysis and machine learning models to create language features based on psycholinguistic hypotheses and evaluate their predictive strength. The third step is the application of a hypothesis-free deep learning approach to the classification of PTSD in our cohort. Results show that the clinical psychiatrist achieved a diagnosis of PTSD with an AUC of 0.72. This is comparable to a gold standard questionnaire (Area Under Curve (AUC) ≈ 0.80). The machine learning model achieved a diagnostic AUC of 0.69. The deep learning approach achieved an AUC of 0.64. An examination of model error informs our discussion. Importantly, the study controls for confounding factors, establishes associations between language and DSM-5 subsymptoms, and integrates automated methods with qualitative analysis. This study provides a direct and methodologically robust description of the relationship between PTSD and language. Our work lays the groundwork for advancing early and accurate diagnosis and using linguistic markers to assess the effectiveness of pharmacological treatments and psychotherapies.
Topics: Stress Disorders, Post-Traumatic; Humans; Deep Learning; Male; Machine Learning; Female; Adult; Language; Natural Language Processing; Biomarkers; Middle Aged
PubMed: 38816468
DOI: 10.1038/s41598-024-61557-7 -
Heliyon May 2024This study aims to explore the effect of eco-innovation and renewable energy on carbon dioxide emissions (CDE) for G7 countries. Using regression models, the results...
This study aims to explore the effect of eco-innovation and renewable energy on carbon dioxide emissions (CDE) for G7 countries. Using regression models, the results reveal that eco-innovation and renewable energy lead to reducing CDE in the presence of governance variables. Additional analysis is conducted to examine whether Hofstede national culture dimensions moderate the nexus of "eco-innovation- carbon emission" and "renewable energy-carbon emission". The results show that individualism, long-term orientation, and indulgence dimensions moderate positively the eco-innovation-carbon emission relationship. Moreover, power distance and uncertainty avoidance dimensions moderate the relationship between renewable energy and CDE and help reduce carbon emissions. The outcomes of this study provide new insights and directives for policymakers and regulators. In fact, increased investment in eco-innovation and renewable energy will support the environmental agenda of G7 countries. National cultural dimensions should be taken into consideration to improve awareness of environmental quality. Moreover, the combination of governance indicators plays a key role in environmental sustainability.
PubMed: 38813154
DOI: 10.1016/j.heliyon.2024.e31142