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Medical Teacher Jun 2024In Thailand, Lesbian, Gay, Bisexual, Transgender, and Queer (LGBTQ) individuals face significant health disparities and discrimination in healthcare. A primary cause is...
PURPOSE
In Thailand, Lesbian, Gay, Bisexual, Transgender, and Queer (LGBTQ) individuals face significant health disparities and discrimination in healthcare. A primary cause is the lack of knowledge among doctors and their negative attitudes towards LGBTQ people. The purpose of this study was to explore the current undergraduate medical curricula of medical schools in Thailand concerning learning outcomes, contents, teaching and learning methods, and assessment methods in the field of LGBTQ health. It also sought to gather opinions from principal stakeholders in curriculum development.
METHODS
The authors employed a mixed-methods approach with a convergent design to conduct the research. Quantitative data were collected from 23 deputy deans of educational affairs using a standardized interview form, and qualitative data were obtained through in-depth interviews with key stakeholders including 16 LGBTQ healthcare receivers, 22 medical students, and three medical teachers. Both datasets were analyzed simultaneously to ensure consistency.
RESULTS
The findings indicate that none of the medical schools had established learning objectives related to LGBTQ healthcare within their curricula. Of the institutions surveyed, 8 out of 15 (53.3%) offered some form of teaching on this topic, aligning with the qualitative data which showed 7 out of 17 institutions (41.2%) provided such education. The most frequently covered topics were gender identity and sexual orientation. Lectures were the predominant teaching method, while multiple-choice questions were the most common assessment format. There was a unanimous agreement among all principal stakeholders on the necessity of integrating LGBTQ healthcare into the M.D. program and the professional standards governed by the Thai Medical Council.
CONCLUSIONS
Although some Thai medical schools have begun to incorporate LGBTQ health into their curricula, the approach does not fully address the actual health issues faced by LGBTQ individuals. Future teaching should emphasize fostering positive attitudes towards LGBTQ people and enhancing communication skills, rather than focusing solely on the cognitive aspects of terminology. Importantly, medical educators should serve as role models in providing competent and compassionate care for LGBTQ patients.
PubMed: 38913809
DOI: 10.1080/0142159X.2024.2362240 -
Behavioral Neuroscience Jun 2024There is a growing number of studies investigating discriminatory fear conditioning and conditioned inhibition of fear to assess safety learning, in addition to...
There is a growing number of studies investigating discriminatory fear conditioning and conditioned inhibition of fear to assess safety learning, in addition to extinction of cued fear. Despite all of these paradigms resulting in a reduction in fear expression, there are nuanced differences among them, which could be mediated through distinct behavioral and neural mechanisms. These differences could impact how we approach potential treatment options in clinical disorders with dysregulated fear responses. The objective of this review is to give an overview of the conditional discrimination and inhibition findings reported in both animal models and human neuropsychiatric disorders. Both behavioral and neural findings are reviewed among human and rodent studies that include conditional fear discrimination via conditional stimuli with and without reinforcement (CS+ vs. CS-, respectively) and/or conditional inhibition of fear through assessment of the fear response to a compound CS-/CS+ cue versus CS+. There are several parallels across species in behavioral fear expression as well as neural circuits promoting fear reduction in response to a CS- and/or CS-/CS+ compound cue. Continued and increased efforts to compare similar behavioral fear inhibition paradigms across species are needed to make breakthrough advances in our understanding and treatment approaches to individuals with fear disorders. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
PubMed: 38913706
DOI: 10.1037/bne0000594 -
Neurobiology of Language (Cambridge,... 2024Reading is both a visual and a linguistic task, and as such it relies on both general-purpose, visual mechanisms and more abstract, meaning-oriented processes....
Reading is both a visual and a linguistic task, and as such it relies on both general-purpose, visual mechanisms and more abstract, meaning-oriented processes. Disentangling the roles of these resources is of paramount importance in reading research. The present study capitalizes on the coupling of fast periodic visual stimulation and MEG recordings to address this issue and investigate the role of different kinds of visual and linguistic units in the visual word identification system. We compared strings of pseudo-characters; strings of consonants (e.g., sfcl); readable, but unattested strings (e.g., amsi); frequent, but non-meaningful chunks (e.g., idge); suffixes (e.g., ment); and words (e.g., vibe); and looked for discrimination responses with a particular focus on the ventral, occipito-temporal regions. The results revealed sensitivity to alphabetic, readable, familiar, and lexical stimuli. Interestingly, there was no discrimination between suffixes and equally frequent, but meaningless endings, thus highlighting a lack of sensitivity to semantics. Taken together, the data suggest that the visual word identification system, at least in its early processing stages, is particularly tuned to form-based regularities, most likely reflecting its reliance on general-purpose, statistical learning mechanisms that are a core feature of the visual system as implemented in the ventral stream.
PubMed: 38911459
DOI: 10.1162/nol_a_00145 -
Nature Communications Jun 2024Metacognitive evaluations of confidence provide an estimate of decision accuracy that could guide learning in the absence of explicit feedback. We examine how humans...
Metacognitive evaluations of confidence provide an estimate of decision accuracy that could guide learning in the absence of explicit feedback. We examine how humans might learn from this implicit feedback in direct comparison with that of explicit feedback, using simultaneous EEG-fMRI. Participants performed a motion direction discrimination task where stimulus difficulty was increased to maintain performance, with intermixed explicit- and no-feedback trials. We isolate single-trial estimates of post-decision confidence using EEG decoding, and find these neural signatures re-emerge at the time of feedback together with separable signatures of explicit feedback. We identified these signatures of implicit versus explicit feedback along a dorsal-ventral gradient in the striatum, a finding uniquely enabled by an EEG-fMRI fusion. These two signals appear to integrate into an aggregate representation in the external globus pallidus, which could broadcast updates to improve cortical decision processing via the thalamus and insular cortex, irrespective of the source of feedback.
Topics: Humans; Decision Making; Male; Magnetic Resonance Imaging; Female; Adult; Basal Ganglia; Young Adult; Learning; Electroencephalography; Brain Mapping
PubMed: 38909014
DOI: 10.1038/s41467-024-49538-w -
The Lancet. Digital Health Jul 2024Pulmonary complications are the most common cause of death after surgery. This study aimed to derive and externally validate a novel prognostic model that can be used...
A prognostic model for use before elective surgery to estimate the risk of postoperative pulmonary complications (GSU-Pulmonary Score): a development and validation study in three international cohorts.
BACKGROUND
Pulmonary complications are the most common cause of death after surgery. This study aimed to derive and externally validate a novel prognostic model that can be used before elective surgery to estimate the risk of postoperative pulmonary complications and to support resource allocation and prioritisation during pandemic recovery.
METHODS
Data from an international, prospective cohort study were used to develop a novel prognostic risk model for pulmonary complications after elective surgery in adult patients (aged ≥18 years) across all operation and disease types. The primary outcome measure was postoperative pulmonary complications at 30 days after surgery, which was a composite of pneumonia, acute respiratory distress syndrome, and unexpected mechanical ventilation. Model development with candidate predictor variables was done in the GlobalSurg-CovidSurg Week dataset (global; October, 2020). Two structured machine learning techniques were explored (XGBoost and the least absolute shrinkage and selection operator [LASSO]), and the model with the best performance (GSU-Pulmonary Score) underwent internal validation using bootstrap resampling. The discrimination and calibration of the score were externally validated in two further prospective cohorts: CovidSurg-Cancer (worldwide; February to August, 2020, during the COVID-19 pandemic) and RECON (UK and Australasia; January to October, 2019, before the COVID-19 pandemic). The model was deployed as an online web application. The GlobalSurg-CovidSurg Week and CovidSurg-Cancer studies were registered with ClinicalTrials.gov, NCT04509986 and NCT04384926.
FINDINGS
Prognostic models were developed from 13 candidate predictor variables in data from 86 231 patients (1158 hospitals in 114 countries). External validation included 30 492 patients from CovidSurg-Cancer (726 hospitals in 75 countries) and 6789 from RECON (150 hospitals in three countries). The overall rates of pulmonary complications were 2·0% in derivation data, and 3·9% (CovidSurg-Cancer) and 4·7% (RECON) in the validation datasets. Penalised regression using LASSO had similar discrimination to XGBoost (area under the receiver operating curve [AUROC] 0·786, 95% CI 0·774-0·798 vs 0·785, 0·772-0·797), was more explainable, and required fewer covariables. The final GSU-Pulmonary Score included ten predictor variables and showed good discrimination and calibration upon internal validation (AUROC 0·773, 95% CI 0·751-0·795; Brier score 0·020, calibration in the large [CITL] 0·034, slope 0·954). The model performance was acceptable on external validation in CovidSurg-Cancer (AUROC 0·746, 95% CI 0·733-0·760; Brier score 0·036, CITL 0·109, slope 1·056), but with some miscalibration in RECON data (AUROC 0·716, 95% CI 0·689-0·744; Brier score 0·045, CITL 1·040, slope 1·009).
INTERPRETATION
This novel prognostic risk score uses simple predictor variables available at the time of a decision for elective surgery that can accurately stratify patients' risk of postoperative pulmonary complications, including during SARS-CoV-2 outbreaks. It could inform surgical consent, resource allocation, and hospital-level prioritisation as elective surgery is upscaled to address global backlogs.
FUNDING
National Institute for Health Research.
Topics: Humans; Elective Surgical Procedures; Postoperative Complications; Female; Prognosis; Middle Aged; Male; Prospective Studies; Aged; COVID-19; Risk Assessment; Adult; Machine Learning; Risk Factors; Lung Diseases; Cohort Studies
PubMed: 38906616
DOI: 10.1016/S2589-7500(24)00065-7 -
Lung Cancer (Amsterdam, Netherlands) Jun 2024To establish and validate a clinical model for differentiating peripheral lung cancer (PLC) from solitary pulmonary tuberculosis (SP-TB) based on clinical and imaging...
OBJECTIVE
To establish and validate a clinical model for differentiating peripheral lung cancer (PLC) from solitary pulmonary tuberculosis (SP-TB) based on clinical and imaging features.
MATERIALS AND METHODS
Retrospectively, 183 patients (100 PLC, 83 SP-TB) in our hospital were randomly divided into a training group and an internal validation group (ratio 7:3), and 100 patients (50 PLC, 50 SP-TB) in Sichuan Provincial People's Hospital were identified as an external validation group. The collected qualitative and quantitative variables were used to determine the independent feature variables for distinguishing between PLC and SP-TB through univariate logistic regression, multivariate logistic regression. Then, traditional logistic regression models and machine learning algorithm models (decision tree, random forest, xgboost, support vector machine, k-nearest neighbors, light gradient boosting machine) were established using the independent feature variables. The model with the highest AUC value in the internal validation group was used for subsequent analysis. The receiver operating characteristic curve (ROC), calibration curve, and decision curves analysis (DCA) were used to assess the model's discrimination, calibration, and clinical usefulness.
RESULT
Age, smoking history, maximum diameter of lesion, lobulation, spiculation, calcification, and vascular convergence sign were independent characteristic variables to differentiate PLC from SP-TB. The logistic regression model had the highest AUC value of 0.878 for the internal validation group, based on which a quantitative visualization nomogram was constructed to discriminate the two diseases. The area under the ROC curve (AUC) of the model in the training, internal validation, and external validation groups were 0.915 (95 % CI: 0.866-0.965), 0.878 (95 % CI: 0.784-0.971), and 0.912 (95 % CI: 0.855-0.969), respectively, and the calibration curves fitted well. Decision curves analysis (DCA) confirmed the good clinical benefit of the model.
CONCLUSION
The model constructed based on clinical and imaging features can accurately differentiate between PLC and SP-TB, providing potential value for developing reasonable clinical plans.
PubMed: 38905954
DOI: 10.1016/j.lungcan.2024.107851 -
European Radiology Jun 2024This study investigated the diagnostic performance of dual-energy computed tomography (CT) and deep learning for the preoperative classification of equivocal lymph nodes...
OBJECTIVES
This study investigated the diagnostic performance of dual-energy computed tomography (CT) and deep learning for the preoperative classification of equivocal lymph nodes (LNs) on CT images in thyroid cancer patients.
METHODS
In this prospective study, from October 2020 to March 2021, 375 patients with thyroid disease underwent thin-section dual-energy thyroid CT at a small field of view (FOV) and thyroid surgery. The data of 183 patients with 281 LNs were analyzed. The targeted LNs were negative or equivocal on small FOV CT images. Six deep-learning models were used to classify the LNs on conventional CT images. The performance of all models was compared with pathology reports.
RESULTS
Of the 281 LNs, 65.5% had a short diameter of less than 4 mm. Multiple quantitative dual-energy CT parameters significantly differed between benign and malignant LNs. Multivariable logistic regression analyses showed that the best combination of parameters had an area under the curve (AUC) of 0.857, with excellent consistency and discrimination, and its diagnostic accuracy and sensitivity were 74.4% and 84.2%, respectively (p < 0.001). The visual geometry group 16 (VGG16) based model achieved the best accuracy (86%) and sensitivity (88%) in differentiating between benign and malignant LNs, with an AUC of 0.89.
CONCLUSIONS
The VGG16 model based on small FOV CT images showed better diagnostic accuracy and sensitivity than the spectral parameter model. Our study presents a noninvasive and convenient imaging biomarker to predict malignant LNs without suspicious CT features in thyroid cancer patients.
CLINICAL RELEVANCE STATEMENT
Our study presents a deep-learning-based model to predict malignant lymph nodes in thyroid cancer without suspicious features on conventional CT images, which shows better diagnostic accuracy and sensitivity than the regression model based on spectral parameters.
KEY POINTS
Many cervical lymph nodes (LNs) do not express suspicious features on conventional computed tomography (CT). Dual-energy CT parameters can distinguish between benign and malignant LNs. Visual geometry group 16 model shows superior diagnostic accuracy and sensitivity for malignant LNs.
PubMed: 38904758
DOI: 10.1007/s00330-024-10854-w -
Archives of Microbiology Jun 2024In this study, we propose an Ethanol Pretreatment Gram staining method that significantly enhances the color contrast of the stain, thereby improving the accuracy of...
In this study, we propose an Ethanol Pretreatment Gram staining method that significantly enhances the color contrast of the stain, thereby improving the accuracy of judgement, and demonstrated the effectiveness of the modification by eliminating unaided-eye observational errors with unsupervised machine learning image analysis. By comparing the traditional Gram staining method with the improved method on various bacterial samples, results showed that the improved method offers distinct color contrast. Using multimodal assessment strategies, including unaided-eye observation, manual image segmentation, and advanced unsupervised machine learning automatic image segmentation, the practicality of ethanol pretreatment on Gram staining was comprehensively validated. In our quantitative analysis, the application of the CIEDE2000, and CMC color difference standards confirmed the significant effect of the method in enhancing the discrimination of Gram staining.This study not only improved the efficacy of Gram staining, but also provided a more accurate and standardized strategy for analyzing Gram staining results, which might provide an useful analytical tool in microbiological diagnostics.
Topics: Ethanol; Staining and Labeling; Unsupervised Machine Learning; Image Processing, Computer-Assisted; Gentian Violet; Phenazines; Bacteria
PubMed: 38904719
DOI: 10.1007/s00203-024-04045-w -
Annals of Medicine Dec 2024Patients with hip fractures frequently need to receive perioperative transfusions of concentrated red blood cells due to preoperative anemia or surgical blood loss....
BACKGROUND
Patients with hip fractures frequently need to receive perioperative transfusions of concentrated red blood cells due to preoperative anemia or surgical blood loss. However, the use of perioperative blood products increases the risk of adverse events, and the shortage of blood products is prompting us to minimize blood transfusion. Our study aimed to construct a machine learning algorithm predictive model to identify patients at high risk for perioperative transfusion early in hospital admission and to manage their patient blood to reduce transfusion requirements.
METHODS
This study collected patients hospitalized for hip fractures at a university hospital from May 2016 to November 2022. All patients included in the analysis were randomly divided into a training set and validation set according to 70:30. Eight machine learning algorithms, CART, GBM, KNN, LR, NNet, RF, SVM, and XGBoost, were used to construct the prediction models. The models were evaluated for discrimination, calibration, and clinical utility, and the best prediction model was selected.
RESULTS
A total of 805 patients were included in the study, of whom 306 received transfusions during the perioperative period. We screened eight features used to construct the prediction model: age, fracture time, fracture type, hemoglobin, albumin, creatinine, calcium ion, and activated partial thromboplastin time. After evaluating and comparing the performance of each of the eight models, the model constructed by the XGBoost algorithm had the best performance, with MCC values of 0.828 and 0.939 in the training and validation sets, respectively. In addition, it had good calibration and clinical utility in both the training and validation sets.
CONCLUSION
The model constructed by the XGBoost algorithm has the best performance, using this model to identify patients at high risk for transfusion early in their admission and promptly incorporating them into a patient blood management plan can help reduce the risk of transfusion.
Topics: Humans; Male; Hip Fractures; Aged; Machine Learning; Female; Blood Transfusion; Aged, 80 and over; Risk Assessment; Blood Loss, Surgical; Algorithms; Perioperative Care; Risk Factors
PubMed: 38902847
DOI: 10.1080/07853890.2024.2357225 -
American Journal of Speech-language... Jun 2024Ableism is a pervasive set of beliefs that regard nondisabled bodies and minds as ideal and necessary to live a full life. Ableism manifests for people with aphasia as...
PURPOSE
Ableism is a pervasive set of beliefs that regard nondisabled bodies and minds as ideal and necessary to live a full life. Ableism manifests for people with aphasia as stigma and discrimination based on their language ability. We assert that ableism contributes to decreased quality of life for people with aphasia and should be actively challenged and disrupted by clinicians and researchers in the field.
METHOD
We applied the Health Stigma and Discrimination Framework (HSDF) to outline how stigma and discrimination are perpetuated against people with aphasia on the basis of language ability and their downstream health and social consequences. We presented this framework at the Clinical Aphasiology Conference in 2023 and share themes and challenges that arose from this discussion and from our ongoing learning.
DISCUSSION
Applying the HSDF to aphasia outlined potential sequelae of ableism. We identified preliminary foci of future initiatives aimed at challenging ableist beliefs and practices and means to monitor the effectiveness of such interventions. Furthermore, we draw attention to the seeming tension between anti-ableist practices and traditional language rehabilitation goals. We assert that this tension may be a catalyst for fruitful discourse on how clinicians and researchers can resist ableism while honoring the lived experiences of people with aphasia and their goals for language rehabilitation. These discussions may be facilitated by existing models in disability studies (e.g., the political/relational model).
CONCLUSIONS
Clinicians and researchers are well positioned to challenge ableism and minimize the resultant health and social impacts for people living with aphasia. Anti-ableist practices are not antithetical to aphasia rehabilitation and can be thoughtfully integrated into rehabilitation practices and discourse.
PubMed: 38901004
DOI: 10.1044/2024_AJSLP-23-00456