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PloS One 2024This body image study tests the viability of transferring a complex psychophysical paradigm from a controlled in-person laboratory task to an online environment. 172...
This body image study tests the viability of transferring a complex psychophysical paradigm from a controlled in-person laboratory task to an online environment. 172 female participants made online judgements about their own body size when viewing images of computer-generated female bodies presented in either in front-view or at 45-degrees in a method of adjustment (MOA) paradigm. The results of these judgements were then compared to the results of two laboratory-based studies (with 96 and 40 female participants respectively) to establish three key findings. Firstly, the results show that the accuracy of online and in-lab estimates of body size are comparable, secondly that the same patterns of visual biases in judgements are shown both in-lab and online, and thirdly online data shows the same view-orientation advantage in accuracy in body size judgements as the laboratory studies. Thus, this study suggests that that online sampling potentially represents a rapid and accurate way of collecting reliable complex behavioural and perceptual data from a more diverse range of participants than is normally sampled in laboratory-based studies. It also offers the potential for designing stratified sampling strategies to construct a truly representative sample of a target population.
Topics: Humans; Female; Body Image; Adult; Psychophysics; Young Adult; Adolescent; Body Size; Visual Perception; Judgment; Internet
PubMed: 38857270
DOI: 10.1371/journal.pone.0302747 -
Cancer Management and Research 2024In situations where pathological acquisition is difficult, there is a lack of consensus on distinguishing between adenocarcinoma and squamous cell carcinoma from imaging...
PURPOSE
In situations where pathological acquisition is difficult, there is a lack of consensus on distinguishing between adenocarcinoma and squamous cell carcinoma from imaging images, and each doctor can only make judgments based on their own experience. This study aims to extract imaging features of chest CT, extract sensitive factors through logistic univariate and multivariate analysis, and model to distinguish between lung squamous cell carcinoma and lung adenocarcinoma.
METHODS
We downloaded chest CT scans with clear diagnosis of adenocarcinoma and squamous cell carcinoma from The Cancer Imaging Archive (TCIA), extracted 19 imaging features by a radiologist and a thoracic surgeon, including location, spicule, lobulation, cavity, vacuolar sign, necrosis, pleural traction sign, vascular bundle sign, air bronchogram sign, calcification, enhancement degree, distance from pulmonary hilum, atelectasis, pulmonary hilum and bronchial lymph nodes, mediastinal lymph nodes, interlobular septal thickening, pulmonary metastasis, adjacent structures invasion, pleural effusion. Firstly, we apply the glm function of R language to perform logistic univariate analysis on all variables to select variables with P < 0.1. Then, perform logistic multivariate analysis on the selected variables to obtain a predictive model. Next, use the roc function in R language to calculate the AUC value and draw the ROC curve, use the val.prob function in R language to draw the Calibrat curve, and use the rmda package in R language to draw the DCA curve and clinical impact curve. At the same time, 45 patients diagnosed with lung squamous cell carcinoma and lung adenocarcinoma through surgery or biopsy in the Radiotherapy Department and Thoracic Surgery Department of our hospital from 2023 to 2024 were included in the validation group. The chest CT features were jointly determined and recorded by the two doctors mentioned above and included in the validation group. The included image feature data are complete and does not require preprocessing, so directly entering statistical calculations. Perform ROC curves, calibration curves, DCA, and clinical impact curves in the validation group to further validate the predictive model. If the predictive model performs well in the validation group, further draw a nomogram to demonstrate.
RESULTS
This study extracted 19 imaging features from the chest CT scans of 75 patients downloaded from TCIA and finally selected 18 complete data for analysis. First, univariate analysis and multivariate analysis were performed, and a total of 5 variables were obtained: spicule, necrosis, air bronchogram Sign, atelectasis, pulmonary hilum and bronchial lymph nodes. After conducting modeling analysis with AUC = 0.887, a validation group was established using clinical cases from our hospital, Draw ROC curve with AUC = 0.865 in the validation group, evaluate the accuracy of the model through Calibrate calibration curve, evaluate the reliability of the model in clinical practice through DCA curve, and further evaluate the practicality of the model in clinical practice through clinical impact curve.
CONCLUSION
It is possible to extract influential features from ordinary chest CT scans to determine lung adenocarcinoma and squamous cell carcinoma. The model we have set up performs well in terms of discrimination, accuracy, reliability, and practicality.
PubMed: 38855330
DOI: 10.2147/CMAR.S462951 -
Cureus May 2024This comprehensive literature review explores the transformative impact of artificial intelligence (AI) predictive analytics on healthcare, particularly in improving... (Review)
Review
This comprehensive literature review explores the transformative impact of artificial intelligence (AI) predictive analytics on healthcare, particularly in improving patient outcomes regarding disease progression, treatment response, and recovery rates. AI, encompassing capabilities such as learning, problem-solving, and decision-making, is leveraged to predict disease progression, optimize treatment plans, and enhance recovery rates through the analysis of vast datasets, including electronic health records (EHRs), imaging, and genetic data. The utilization of machine learning (ML) and deep learning (DL) techniques in predictive analytics enables personalized medicine by facilitating the early detection of conditions, precision in drug discovery, and the tailoring of treatment to individual patient profiles. Ethical considerations, including data privacy, bias, and accountability, emerge as vital in the responsible implementation of AI in healthcare. The findings underscore the potential of AI predictive analytics in revolutionizing clinical decision-making and healthcare delivery, emphasizing the necessity of ethical guidelines and continuous model validation to ensure its safe and effective use in augmenting human judgment in medical practice.
PubMed: 38854327
DOI: 10.7759/cureus.59954 -
Cureus May 2024The pharmacovigilance program of India (PvPI), after its inception, has been reliably acquiring force in bringing issues to light among the masses, healthcare... (Review)
Review
The pharmacovigilance program of India (PvPI), after its inception, has been reliably acquiring force in bringing issues to light among the masses, healthcare professionals, the pharma industry, and clinical staff at hospitals. Adverse drug reactions are unintended events that occur after exposure to a drug, biological product, or medical device, and they may result in morbidity and mortality. It is critical to monitor the safety of drugs during the post-marketing phase to find long-term and rare ADRs, as well as ADRs in special populations and patients with co-morbidities that are not usually included during clinical trials. The definitive objective of pharmacovigilance is to collate data and analyze it. Assessing the causality between ADRs and drugs is necessary to decrease the occurrence of ADRs and to reduce the risk of drug-related ADRs. ADRs may lead to increased morbidity, increased hospital stays, and increased cost of treatment, resulting in compromised patient safety. Causality assessment is the evaluation of the likelihood that a particular treatment is the cause of an observed adverse event and establishing a causal association between a drug and a drug reaction is necessary to prevent further recurrences. Numerous methods available for establishing a causal association between the drug and adverse events have been broadly classified into clinical judgment or global introspection, algorithms, and probabilistic methods. These include the Swedish method, World Health Organization-Uppsala Monitoring Centre (WHO-UMC) scale, Naranjo's algorithm, Kramer algorithm, Jones algorithm, Karch algorithm, Bégaud algorithm, Adverse Drug Reactions Advisory Committee guidelines, Bayesian Adverse Reaction Diagnostic Instrument, and so on. Despite various methods available, none of the causality assessment tools have been universally accepted as the gold standard. Naranjo's algorithm and WHO-UMC scales are, however, the most commonly used. Similarly, for preventability and severity assessment of ADRs, the Schumock and Thornton scale and Hartwig and Siegel's scale are most commonly used. Hence, we reviewed different tools and methods available to assess the causality, preventability, and severity of ADRs.
PubMed: 38854273
DOI: 10.7759/cureus.59975 -
Scientific Reports Jun 2024When a single choice impacts on life outcomes, faculties to make ethical judgments come into play. Here we studied decisions in a real-life setting involving...
When a single choice impacts on life outcomes, faculties to make ethical judgments come into play. Here we studied decisions in a real-life setting involving life-and-death outcomes that affect others and the decision-maker as well. We chose a genuine situation where prior training and expertise play a role: firefighting in life-threatening situations. By studying the neural correlates of dilemmas involving life-saving decisions, using realistic firefighting situations, allowed us to go beyond previously used hypothetical dilemmas, while addressing the role of expertise and the use of coping strategies (n = 47). We asked the question whether the neural underpinnings of deontologically based decisions are affected by expertise. These realistic life-saving dilemmas activate the same core reward and affective processing network, in particular the ventromedial prefrontal cortex, nucleus accumbens and amygdala, irrespective of prior expertise, thereby supporting general domain theories of ethical decision-making. We found that brain activity in the hippocampus and insula parametrically increased as the risk increased. Connectivity analysis showed a larger directed influence of the insula on circuits related to action selection in non-experts, which were slower than experts in non rescuing decisions. Relative neural activity related to the decision to rescue or not, in the caudate nucleus, insula and anterior cingulate cortex was negatively associated with coping strategies, in experts (firefighters) suggesting practice-based learning. This shows an association between activity and expert-related usage of coping strategies. Expertise enables salience network activation as a function of behavioural coping dimensions, with a distinct connectivity profile when facing life-rescuing dilemmas.
Topics: Humans; Firefighters; Decision Making; Male; Adult; Female; Magnetic Resonance Imaging; Brain; Adaptation, Psychological; Brain Mapping; Prefrontal Cortex
PubMed: 38851794
DOI: 10.1038/s41598-024-63469-y -
Physiology & Behavior Jun 2024Muscle testing is an integral component in assessing musculoskeletal function and tailoring rehabilitation efforts. This study aimed i. to identify an objective... (Review)
Review
Muscle testing is an integral component in assessing musculoskeletal function and tailoring rehabilitation efforts. This study aimed i. to identify an objective evaluation system sensitive to analyze changes in different muscular conditions in different neuromuscular tests across a spectrum of professional experience levels; and ii. to analyze differences in objective parameters and clinical judgment between participants of different levels of expertise in different muscular conditions in different neuromuscular tests. Participants included 60 subjects with Level I to III expertise who performed blinded neuromuscular tests on the middle deltoid and rectus femoris muscles of 40 volunteer subjects. The methodology centered on standardizing test protocols to minimize variability, employing EMG to quantify muscle activity, thermography to capture thermographic muscular response, and digital dynamometry to measure muscular resistance. The findings revealed that while traditional methods like thermography and electromyography provide valuable insights, digital dynamometry stands out for its sensitivity in detecting muscle condition changes in neuromuscular test. Moreover, the data underscored the pivotal role of advanced training and expertise in enhancing the precision and accuracy of neuromuscular diagnostics, since there were significant differences in objective parameters and clinical judgment between participants of different levels of expertise in the different muscular conditions in Middle deltoid and Rectus femoris neuromuscular tests analyzed, presenting higher expertise participant clinical judgment like objective validated instrument.
PubMed: 38851442
DOI: 10.1016/j.physbeh.2024.114602 -
BMC Rheumatology Jun 2024In recent times, there has been acknowledgment of the prevalence of frailty and pre-frailty among individuals with rheumatoid arthritis (RA). Comprehensive Rheumatologic...
BACKGROUND
In recent times, there has been acknowledgment of the prevalence of frailty and pre-frailty among individuals with rheumatoid arthritis (RA). Comprehensive Rheumatologic Assessment of Frailty (CRAF) stands out as a dependable tool grounded in synthesis and clinical judgment. Despite this, a validated Vietnamese rendition of the CRAF is currently unavailable. This study seeks to assess the reliability and validity of the CRAF in a patient with RA in Vietnam.
METHODS
A cross-sectional investigation was carried out with 402 patients diagnosed with rheumatoid arthritis, encompassing both inpatients and outpatients at the Centre for Rheumatology at Bach Mai Hospital in Hanoi, Vietnam. CRAF was employed to gauge the extent of frailty. To establish convergent validity, the scores from the CRAF were correlated with those from the Fried phenotype. Discriminant validity was ascertained through the utilization of receiver operating characteristic (ROC) curve analysis. Additionally, a multivariate logistic regression model was applied to evaluate the individual determinants' relative impact on the CRAF.
RESULTS
In testing for convergent validity, a significant correlation was found between CRAF and Fried phenotype (p < 0.001). The discriminatory power of CRAF was higher than those of the Fried phenotype (difference between areas under the ROC curves = 0.947 (95% CI: 0.927-0.967). Variables associated with frailty at the multivariate analysis were comorbitidy, medication intake, BMI, DAS28-CRP, and age (all at p < 0.01).
CONCLUSION
CRAF exhibited strong validity and accurate discrimination. Incorporating frailty assessment into regular rheumatological practices could signify a significant advancement in the care of rheumatoid arthritis.
PubMed: 38849947
DOI: 10.1186/s41927-024-00394-7 -
BMC Medical Education Jun 2024Clinical observation conducted during the 3rd and 4th years of dental school is an important part of dental students' clinical education. However, conventional clinical... (Comparative Study)
Comparative Study
BACKGROUND
Clinical observation conducted during the 3rd and 4th years of dental school is an important part of dental students' clinical education. However, conventional clinical observation is associated with several problems, including the lack of opportunity for all students to assist during surgery. Virtual reality (VR) technologies and devices can be used to demonstrate clinical processes that dental students need to learn through clinical observation. This study aimed to evaluate the effectiveness of teaching dental students the surgical tooth extraction procedure through clinical observation using VR.
METHODS
We recruited third- and fourth-year dental students and divided them into a VR clinical observation group (VR group) and a conventional clinical observation group (control group). The control group visited an outpatient clinic and observed an oral and maxillofacial specialist perform surgical tooth extraction, whereas the VR group watched a 360° video of surgical tooth extraction using a head-mounted display. After observation, both groups were surveyed regarding their satisfaction with the clinical observation and their understanding of the procedure.
RESULTS
Understanding of the procedure and satisfaction with the observation were significantly higher in the VR group than in the control group (p = 0.001 and p = 0.047, respectively). Compared with conventional clinical observation, VR clinical observation improved learning motivation and medical thinking and judgment skills; however, interaction between professors and students was lacking.
CONCLUSIONS
VR clinical observation using 360° videos might be an effective teaching method for students. However, to allow interaction between professors and students during clinical observations, using it along with conventional clinical observation is necessary.
Topics: Humans; Tooth Extraction; Virtual Reality; Education, Dental; Female; Male; Students, Dental; Clinical Competence; Young Adult
PubMed: 38849825
DOI: 10.1186/s12909-024-05605-w -
Scientific Reports Jun 2024Face recognition is a crucial aspect of self-image and social interactions. Previous studies have focused on static images to explore the boundary of self-face...
Face recognition is a crucial aspect of self-image and social interactions. Previous studies have focused on static images to explore the boundary of self-face recognition. Our research, however, investigates the dynamics of face recognition in contexts involving motor-visual synchrony. We first validated our morphing face metrics for self-face recognition. We then conducted an experiment using state-of-the-art video processing techniques for real-time face identity morphing during facial movement. We examined self-face recognition boundaries under three conditions: synchronous, asynchronous, and static facial movements. Our findings revealed that participants recognized a narrower self-face boundary with moving facial images compared to static ones, with no significant differences between synchronous and asynchronous movements. The direction of morphing consistently biased the recognized self-face boundary. These results suggest that while motor information of the face is vital for self-face recognition, it does not rely on movement synchronization, and the sense of agency over facial movements does not affect facial identity judgment. Our methodology offers a new approach to exploring the 'self-face boundary in action', allowing for an independent examination of motion and identity.
Topics: Humans; Female; Male; Facial Recognition; Adult; Young Adult; Face; Movement; Photic Stimulation; Motion; Facial Expression
PubMed: 38849381
DOI: 10.1038/s41598-024-63233-2 -
NeuroImage Aug 2024Humans constantly make predictions and such predictions allow us to prepare for future events. Yet, such benefits may come with drawbacks as premature predictions may...
Humans constantly make predictions and such predictions allow us to prepare for future events. Yet, such benefits may come with drawbacks as premature predictions may potentially bias subsequent judgments. Here we examined how prediction influences our perceptual decisions and subsequent confidence judgments, on scenarios where the predictions were arbitrary and independent of the identity of the upcoming stimuli. We defined them as invalid and non-informative predictions. Behavioral results showed that, such non-informative predictions biased perceptual decisions in favor of the predicted choice, and such prediction-induced perceptual bias further increased the metacognitive efficiency. The functional MRI results showed that activities in the medial prefrontal cortex (mPFC) and subgenual anterior cingulate cortex (sgACC) encoded the response consistency between predictions and perceptual decisions. Activity in mPFC predicted the strength of this congruency bias across individuals. Moreover, the parametric encoding of confidence in putamen was modulated by prediction-choice consistency, such that activity in putamen was negatively correlated with confidence rating after inconsistent responses. These findings suggest that predictions, while made arbitrarily, orchestrate the neural representations of choice and confidence judgment.
Topics: Humans; Male; Magnetic Resonance Imaging; Female; Metacognition; Young Adult; Adult; Prefrontal Cortex; Brain Mapping; Judgment; Gyrus Cinguli; Choice Behavior
PubMed: 38848980
DOI: 10.1016/j.neuroimage.2024.120670