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Omega Oct 2023The COVID-19 pandemic continues to have an unprecedented impact on people's lives and the economy worldwide. Vaccines are the strongest evidence-based defense against...
The COVID-19 pandemic continues to have an unprecedented impact on people's lives and the economy worldwide. Vaccines are the strongest evidence-based defense against the spread of the disease. The release of COVID-19 vaccines to the general public created policy challenges associated with how to best allocate vaccines among different sub-regions. In the United States, after vaccines became widely available for all eligible adults, policymakers faced objectives such as () achieving an equitable allocation to reduce populations' travel times to get vaccinated and () effectively allocating vaccine doses to minimize waste and unmet need. This problem was further exacerbated by the underlying factors of population vaccine hesitancy and sub-regions' varying capacity levels to administer vaccines to eligible and willing populations. Although simple to implement, commonly used pro rata policies do not capture the complexities of this problem. We propose two alternatives to simple pro rata policies. The first alternative is based on a Mixed-Integer Linear Programming Model that minimizes the maximum travel duration of patients and aims to achieve an equitable and effective allocation of vaccines to sub-regions while considering capacity and vaccine hesitancy. A second alternative is a heuristic approach that may be more palatable for policymakers who () are not familiar with mathematical modeling, () are reluctant to use black-box models, and () prefer algorithms that are easy to understand and implement. We demonstrate the results of our model through a case study based on real data from the state of Alabama and show that substantial improvements in travel time-based equity are achievable through capacity improvements in a small subset of counties. We perform additional computational experiments that compare the proposed methods in terms of several metrics and demonstrate the promising performance of our model and proposed heuristic. We find that while our mathematical model can achieve equitable and effective vaccine allocation, the proposed heuristic performs better if the goal is to minimize average travel duration. Finally, we explore two model extensions that aim to () lower vaccine hesitancy by allocating vaccines, and () prioritize vaccine access for certain high-risk sub-populations.
PubMed: 37275337
DOI: 10.1016/j.omega.2023.102898 -
Cureus Mar 2024Introduction Artificial intelligence (AI) models using large language models (LLMs) and non-specific domains have gained attention for their innovative information...
Introduction Artificial intelligence (AI) models using large language models (LLMs) and non-specific domains have gained attention for their innovative information processing. As AI advances, it's essential to regularly evaluate these tools' competency to maintain high standards, prevent errors or biases, and avoid flawed reasoning or misinformation that could harm patients or spread inaccuracies. Our study aimed to determine the performance of Chat Generative Pre-trained Transformer (ChatGPT) by OpenAI and Google BARD (BARD) in orthopedic surgery, assess performance based on question types, contrast performance between different AIs and compare AI performance to orthopedic residents. Methods We administered ChatGPT and BARD 757 Orthopedic In-Training Examination (OITE) questions. After excluding image-related questions, the AIs answered 390 multiple choice questions, all categorized within 10 sub-specialties (basic science, trauma, sports medicine, spine, hip and knee, pediatrics, oncology, shoulder and elbow, hand, and food and ankle) and three taxonomy classes (recall, interpretation, and application of knowledge). Statistical analysis was performed to analyze the number of questions answered correctly by each AI model, the performance returned by each AI model within the categorized question sub-specialty designation, and the performance of each AI model in comparison to the results returned by orthopedic residents classified by their respective post-graduate year (PGY) level. Results BARD answered more overall questions correctly (58% vs 54%, p<0.001). ChatGPT performed better in sports medicine and basic science and worse in hand surgery, while BARD performed better in basic science (p<0.05). The AIs performed better in recall questions compared to the application of knowledge (p<0.05). Based on previous data, it ranked in the 42nd-96th percentile for post-graduate year ones (PGY1s), 27th-58th for PGY2s, 3rd-29th for PGY3s, 1st-21st for PGY4s, and 1st-17th for PGY5s. Discussion ChatGPT excelled in sports medicine but fell short in hand surgery, while both AIs performed well in the basic science sub-specialty but performed poorly in the application of knowledge-based taxonomy questions. BARD performed better than ChatGPT overall. Although the AI reached the second-year PGY orthopedic resident level, it fell short of passing the American Board of Orthopedic Surgery (ABOS). Its strengths in recall-based inquiries highlight its potential as an orthopedic learning and educational tool.
PubMed: 38618358
DOI: 10.7759/cureus.56104 -
Computational and Structural... Dec 2024Piwi-interacting RNAs (piRNAs) are a class of small non-coding RNAs (ncRNAs) that plays important roles in many biological processes and major cancer diagnosis and... (Review)
Review
Piwi-interacting RNAs (piRNAs) are a class of small non-coding RNAs (ncRNAs) that plays important roles in many biological processes and major cancer diagnosis and treatment, thus becoming a hot research topic. This study aims to provide an in-depth review of computational piRNA-related research, including databases and computational models. Herein, we perform literature analysis and use comparative evaluation methods to summarize and analyze three aspects of computational piRNA-related research: (i) computational models for piRNA-related molecular identification tasks, (ii) computational models for piRNA-disease association prediction tasks, and (iii) computational resources and evaluation metrics for these tasks. This study shows that computational piRNA-related research has significantly progressed, exhibiting promising performance in recent years, whereas they also suffer from the emerging challenges of inconsistent naming systems and the lack of data. Different from other reviews on piRNA-related identification tasks that focus on the organization of datasets and computational methods, we pay more attention to the analysis of computational models, algorithms, and performances that aim to provide valuable references for computational piRNA-related identification tasks. This study will benefit the theoretical development and practical application of piRNAs by better understanding computational models and resources to investigate the biological functions and clinical implications of piRNA.
PubMed: 38328006
DOI: 10.1016/j.csbj.2024.01.011 -
European Journal of Radiology Open Jun 2024To evaluate artificial intelligence-based computer-aided diagnosis (AI-CAD) for screening mammography, we analyzed the diagnostic performance of radiologists by...
PURPOSE
To evaluate artificial intelligence-based computer-aided diagnosis (AI-CAD) for screening mammography, we analyzed the diagnostic performance of radiologists by providing and withholding AI-CAD results alternatively every month.
METHODS
This retrospective study was approved by the institutional review board with a waiver for informed consent. Between August 2020 and May 2022, 1819 consecutive women (mean age 50.8 ± 9.4 years) with 2061 screening mammography and ultrasound performed on the same day in a single institution were included. Radiologists interpreted screening mammography in clinical practice with AI-CAD results being provided or withheld alternatively by month. The AI-CAD results were retrospectively obtained for analysis even when withheld from radiologists. The diagnostic performances of radiologists and stand-alone AI-CAD were compared and the performances of radiologists with and without AI-CAD assistance were also compared by cancer detection rate, recall rate, sensitivity, specificity, accuracy and area under the receiver-operating-characteristics curve (AUC).
RESULTS
Twenty-nine breast cancer patients and 1790 women without cancers were included. Diagnostic performances of the radiologists did not significantly differ with and without AI-CAD assistance. Radiologists with AI-CAD assistance showed the same sensitivity (76.5%) and similar specificity (92.3% vs 93.8%), AUC (0.844 vs 0.851), and recall rates (8.8% vs. 7.4%) compared to standalone AI-CAD. Radiologists without AI-CAD assistance showed lower specificity (91.9% vs 94.6%) and accuracy (91.5% vs 94.1%) and higher recall rates (8.6% vs 5.9%, all p < 0.05) compared to stand-alone AI-CAD.
CONCLUSION
Radiologists showed no significant difference in diagnostic performance when both screening mammography and ultrasound were performed with or without AI-CAD assistance for mammography. However, without AI-CAD assistance, radiologists showed lower specificity and accuracy and higher recall rates compared to stand-alone AI-CAD.
PubMed: 38293282
DOI: 10.1016/j.ejro.2023.100545 -
Scientific Reports Jul 2023Ultrasonic vocalizations (USVs) analysis represents a fundamental tool to study animal communication. It can be used to perform a behavioral investigation of mice for...
Ultrasonic vocalizations (USVs) analysis represents a fundamental tool to study animal communication. It can be used to perform a behavioral investigation of mice for ethological studies and in the field of neuroscience and neuropharmacology. The USVs are usually recorded with a microphone sensitive to ultrasound frequencies and then processed by specific software, which help the operator to identify and characterize different families of calls. Recently, many automated systems have been proposed for automatically performing both the detection and the classification of the USVs. Of course, the USV segmentation represents the crucial step for the general framework, since the quality of the call processing strictly depends on how accurately the call itself has been previously detected. In this paper, we investigate the performance of three supervised deep learning methods for automated USV segmentation: an Auto-Encoder Neural Network (AE), a U-NET Neural Network (UNET) and a Recurrent Neural Network (RNN). The proposed models receive as input the spectrogram associated with the recorded audio track and return as output the regions in which the USV calls have been detected. To evaluate the performance of the models, we have built a dataset by recording several audio tracks and manually segmenting the corresponding USV spectrograms generated with the Avisoft software, producing in this way the ground-truth (GT) used for training. All three proposed architectures demonstrated precision and recall scores exceeding [Formula: see text], with UNET and AE achieving values above [Formula: see text], surpassing other state-of-the-art methods that were considered for comparison in this study. Additionally, the evaluation was extended to an external dataset, where UNET once again exhibited the highest performance. We suggest that our experimental results may represent a valuable benchmark for future works.
Topics: Animals; Mice; Deep Learning; Algorithms; Neural Networks, Computer; Software; Animal Communication
PubMed: 37433808
DOI: 10.1038/s41598-023-38186-7 -
European Journal of Investigation in... Oct 2023Aiming to identify the ideal suhoor timing for maintaining optimal physical performance and health indicators during Ramadan intermittent fasting, the present study...
Aiming to identify the ideal suhoor timing for maintaining optimal physical performance and health indicators during Ramadan intermittent fasting, the present study compares the effects of early vs. late Suhoor on short-term high-intensity physical exercise while controlling the body mass index (BMI) oral temperature (OT), dietary intake, and sleep patterns. In a randomized design, 19 female pre-university handball players (age: 16.8 ± 0.4 y; height: 1.70 ± 0.9 m; and body mass: 61.5 ± 6.9 kg) underwent two test sessions (at 08:00 a.m. and 05:00 p.m.) at four different conditions: ten days prior to Ramadan (R - 10), the final ten days of Ramadan (R) including both Early Suhoor R(ES) and Late Suhoor R(LS) conditions, and the ten days immediately following Ramadan (R + 10). A recovery period of at least 48 h has been set between successive test sessions at each period. Outcome measures included the Countermovement Jumps Test (CMJ), Modified Agility -Test (MATT), Repeated Sprint Ability (RSA), and Rating of Perceived Exertion (RPE). The Pittsburgh Sleep Quality Index (PSQI), OT, BMI, and daily diary intake were assessed across the three periods. The total scores of PSQI decreased significantly during R and R + 10 compared to R - 10. When performed in the afternoon, CMJ, MATT, and RSA performance decreased significantly at R(ES) and R(LS) conditions compared to R - 10. However, these performances decreased only after R(ES) when performed in the morning. Furthermore, performances were lower during R(ES) compared to R(LS) in the afternoon for all tests and the morning for MATT and RSA tests. These findings support prior research showing a deterioration of physical performance during Ramadan fasting and indicate a more pronounced impact following early Suhoor condition. Therefore, consuming a late suhoor, closer to pre-dawn time, could be suggested as an effective strategy to minimize physical performance decline during short-term high-intensity exercise.
PubMed: 37887153
DOI: 10.3390/ejihpe13100152 -
Heliyon Oct 2023Our study aims to find the more practical and powerful method to predict intracranial aneurysm (IA) rupture through verification of predictive power of different models.
OBJECTIVES
Our study aims to find the more practical and powerful method to predict intracranial aneurysm (IA) rupture through verification of predictive power of different models.
METHODS
Clinical and imaging data of 576 patients with IAs including 192 ruptured IAs and matched 384 unruptured IAs was retrospectively analyzed. Radiomics features derived from computed tomography angiography (CTA) images were selected by -test and Elastic-Net regression. A radiomics score (radscore) was developed based on the optimal radiomics features. Inflammatory markers were selected by multivariate regression. And then 4 models including the radscore, inflammatory, clinical and clinical-radscore models (C-R model) were built. The receiver operating characteristic curve (ROC) was performed to evaluate the performance of each model, PHASES and ELAPSS. The nomogram visualizing the C-R model was constructed to predict the risk of IA rupture.
RESULTS
Five inflammatory features, 2 radiological characteristics and 7 radiomics features were significantly associated with IA rupture. The areas under ROCs of the radscore, inflammatory, clinical and C-R models were 0.814, 0.935, 0.970 and 0.975 in the training cohort and 0.805, 0.927, 0.952 and 0.962 in the validation cohort, respectively.
CONCLUSION
The inflammatory model performs particularly well in predicting the risk of IA rupture, and its predictive power is further improved by combining with radiological and radiomics features and the C-R model performs the best. The C-R nomogram is a more stable and effective tool than PHASES and ELAPSS for individually predicting the risk of rupture for patients with IA.
PubMed: 37842571
DOI: 10.1016/j.heliyon.2023.e20718 -
Canadian Urological Association Journal... Aug 2023Radical cystectomy (RC) is a complex oncological surgical procedure and population studies of routine surgical care have suggested suboptimal results compared to...
INTRODUCTION
Radical cystectomy (RC) is a complex oncological surgical procedure and population studies of routine surgical care have suggested suboptimal results compared to high-volume centers of excellence. A previous Canadian bladder cancer quality-of-care consensus led to adoption of multiple key quality-of-care indicators, with associated benchmarks created using available evidence and expert opinion to inform and measure future performance. Herein, we report real-life benchmark performance for the management of muscle-invasive bladder cancer (MIBC) relative to expert opinion guidance.
METHODS
This is a population-based, retrospective, cohort study that used the Ontario Cancer Registry (OCR) to identify all incident patients who underwent RC from 2009-2013. Electronic records of treatment from 1573 patients were linked to OCR; pathology records were obtained for all cases and reviewed by a team of trained data abstractors. The primary objective was to describe benchmarks for identified indicators, first as median values obtained across hospitals or providers, as well as a "pared-mean" approach to identify a benchmark population of "top performance," as defined as the best outcome accomplished for at least 10% of the population.
RESULTS
Overall, performance in Ontario across all indicators fell short of expert opinion-determined benchmarks. Annual surgical volume by each surgeon performing a RC (benchmark >6, percent of institutions meeting benchmark=20%), percent of patients with MIBC referred preoperatively to medical oncology (MO; benchmark>90%, percent of institutions meeting benchmark=2%) and radiation oncology (RO; benchmark>50%, percent of institutions meeting benchmark=0%), time to cystectomy within six weeks of transurethral resection of bladder tumor (TURBT) in patients without neoadjuvant chemotherapy (benchmark <6 weeks, percent of institutions meeting benchmark=0%), percent of patients with adequate lymph node dissection (defined as >14 nodes, benchmark>85%, percent of institutions meeting benchmark=0%), percent of patients with positive margins post-RC (benchmark <10%, percent of institutions meeting benchmark=46%), and 90-day mortality (benchmark<5%, percent of institutions meeting benchmark=37%) fell considerably short. Simply evaluating benchmarks across the province as median performance significantly underestimated benchmarks that were possible by top-performing hospitals.
CONCLUSIONS
Performance through most bladder cancer quality-of-care indicators fall short of benchmarks proposed by expert opinion. Different methodologies, such as a paredmean approach of top performers, may provide more realistic benchmarking.
PubMed: 37581551
DOI: 10.5489/cuaj.8231 -
Clinical and Experimental Dental... Oct 2023Periodic examination of the head and neck includes screening for oral cancer, which is largely performed in dental offices by vigilant oral healthcare providers. The aim...
OBJECTIVE
Periodic examination of the head and neck includes screening for oral cancer, which is largely performed in dental offices by vigilant oral healthcare providers. The aim of this study was to assess practice patterns among Virginia dentists in performing head and neck exams and the referral rates of biopsies after completion of head and neck exams. We hypothesized that not all dentists perform head and neck exams and there is a difference between dentists who refer patients for a biopsy and those that perform biopsies.
METHODS
General dentists and dental specialists who are members of the Virginia Dental Association were invited to participate in a cross-sectional survey study through REDCap to self-report their head and neck exam protocols.
RESULTS
A total of 224 providers completed the survey. The majority of respondents were general dentists with more than 20 years in practice, who practice in a private setting, and see more than 10 patients in a day. All respondents stated they perform intraoral examinations, but 10 respondents stated they do not perform extraoral examinations. Nearly a third of respondents reported doing their own biopsies.
CONCLUSIONS
Although only 8.5% of oral healthcare providers in Virginia responded to our survey, respondents are following the 2017 ADA good practice statement by providing their patients with head and neck exams to screen for oral cancer. Additional education pertaining to extraoral anatomy, malignant transformation of oral potentially malignant disorders, and pathology procedures may be helpful to clinicians.
Topics: Humans; Cross-Sectional Studies; Mouth Neoplasms; Mouth Diseases; Referral and Consultation; Dentists
PubMed: 37759423
DOI: 10.1002/cre2.772 -
Perspectives on Psychological Science :... Jul 2023Psychologists have studied the ancient concept of wisdom for 3 decades. Nevertheless, apparent discrepancies in theories and empirical findings have left the nomological...
Psychologists have studied the ancient concept of wisdom for 3 decades. Nevertheless, apparent discrepancies in theories and empirical findings have left the nomological network of the construct unclear. Using multilevel meta-analyses, we summarized wisdom's correlations with age, intelligence, the Big Five personality traits, narcissism, self-esteem, social desirability, and well-being. We furthermore examined whether these correlations were moderated by the general approach to conceptualizing and measuring wisdom (i.e., phenomenological wisdom as indexed by self-report vs. performative wisdom as indexed by performance ratings), by specific wisdom measures, and by variable-specific factors (e.g., age range, type of intelligence measures, and well-being type). Although phenomenological and performative approaches to conceptualizing and measuring wisdom had some unique correlates, both were correlated with openness, hedonic well-being, and eudaimonic well-being, especially the growth aspect of eudaimonic well-being. Differences between phenomenological and performative wisdom are discussed in terms of the differences between typical and maximal performance, self-ratings and observer ratings, and global and state wisdom. This article will help move the scientific study of wisdom forward by elucidating reliable wisdom correlates and by offering concrete suggestions for future empirical research based on the meta-analytic findings.
Topics: Humans; Self Concept; Intelligence; Narcissism; Personality
PubMed: 36322834
DOI: 10.1177/17456916221114096