Yoxas/statistical_literacy huggingface dataset:
To determine whether the reform of the first year of medical studies implemented in September 2020 in France met its objective of diversifying the profiles of students admitted to second year at the faculty of medicine at the University of Tours. Single-centered, retrospective study, covering students who passed the first year of medical studies between 2018 and 2022. Student profiles originating from three different entry gateways (PACES, PASS and L.AS) to the second year of medical studies were compared. One thousand four hundred and seventy-nine students over five promotions were included (806 in PACES, 329 in PASS, 198 in L.AS). The ratio of students who had obtained a baccalaureate with high or highest honors was significantly higher in PACES (85%) and PASS (96%) compared to L.AS (66%; p < 0.001). These differences were related to increased student intake via a standard pass in L.AS (21% compared to 3.2% in PACES and 0.9% in PASS) (p < 0.001). In terms of geographical origin, the proportion of students residing in regions outside the University City area increased significantly in L.AS (11%) compared to PACES (1.7%) and PASS (3.3%) (p < 0.001). The mean number of parents from the white-collar and knowledge professional category was significantly higher in PACES (0.91) and PASS (1.06) compared to L.AS (0.80; p < 0.001). Students with a scientific background and who obtained highest honors in their high school diploma, remain the standard in PACES and PASS. Diversification of student profiles was achieved only within the L.AS gateway, which represented 42% of total second year admissions during the post-reform year. Student profile diversification was therefore a partially achieved objective and follow up studies of future promotions is needed to assess the medium and long-term impact of the reform. Particular attention should be paid to the future of these students who have different profiles between L.AS and PASS to determine whether these changes will have any impact in the quality of healthcare for the French population.
To develop a Canadian Internal Medicine Ultrasound (CIMUS) consensus statement on recommended mandatory point-of-care ultrasound (POCUS) competencies for ultrasound-guided thoracentesis, paracentesis, and central venous catheterizations (CVC) for internal medicine physicians. The 2022 CIMUS group consists of 27 voting members, with representations from all 17 Canadian academic institutions across 8 provinces. Members voted in 3 rounds on 46 procedural competencies as "mandatory, must include", "optional, could include" or "superfluous, do not include". These 46 competencies included 6 general competencies that apply to all POCUS-guided procedures, 11 competencies for thoracentesis, 10 competencies for paracentesis, and 19 competencies for CVC. In the first round, members reached consensus on 27 competencies (5 general, 6 thoracentesis, 8 paracentesis, 8 CVC). In the second round, 10 competencies (1 general, 2 thoracentesis, 1 paracentesis, 6 CVC) reached consensus. In the third round, 2 additional competencies (1 paracentesis, 1 CVC) reached consensus for being mandatory and 3 as optional (1 thoracentesis and 2 CVC). Overall, a total of 28 competencies reached consensus as mandatory, 3 as optional, while 11 competencies reached consensus as superfluous. Four competencies did not reach consensus for either inclusion or exclusion. The CIMUS group recommends 28 competencies be considered mandatory and 3 as optional for internal medicine physicians performing POCUS guided thoracentesis, paracentesis, and CVC placement. National curriculum development and implementation efforts should include training these mandatory competencies.
To develop a deep learning approach that enables ultra-low-dose, 1% of the standard clinical dosage (3 MBq/kg), ultrafast whole-body PET reconstruction in cancer imaging. In this Health Insurance Portability and Accountability Act-compliant study, serial fluorine 18-labeled fluorodeoxyglucose PET/MRI scans of pediatric patients with lymphoma were retrospectively collected from two cross-continental medical centers between July 2015 and March 2020. Global similarity between baseline and follow-up scans was used to develop Masked-LMCTrans, a longitudinal multimodality coattentional convolutional neural network (CNN) transformer that provides interaction and joint reasoning between serial PET/MRI scans from the same patient. Image quality of the reconstructed ultra-low-dose PET was evaluated in comparison with a simulated standard 1% PET image. The performance of Masked-LMCTrans was compared with that of CNNs with pure convolution operations (classic U-Net family), and the effect of different CNN encoders on feature representation was assessed. Statistical differences in the structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), and visual information fidelity (VIF) were assessed by two-sample testing with the Wilcoxon signed rank t test. The study included 21 patients (mean age, 15 years ± 7 [SD]; 12 female) in the primary cohort and 10 patients (mean age, 13 years ± 4; six female) in the external test cohort. Masked-LMCTrans-reconstructed follow-up PET images demonstrated significantly less noise and more detailed structure compared with simulated 1% extremely ultra-low-dose PET images. SSIM, PSNR, and VIF were significantly higher for Masked-LMCTrans-reconstructed PET (P < .001), with improvements of 15.8%, 23.4%, and 186%, respectively. Masked-LMCTrans achieved high image quality reconstruction of 1% low-dose whole-body PET images.Keywords: Pediatrics, PET, Convolutional Neural Network (CNN), Dose Reduction Supplemental material is available for this article. © RSNA, 2023.
To develop a disease stratification model for COVID-19 that updates according to changes in a patient's condition while in hospital to facilitate patient management and resource allocation. In this retrospective cohort study, we adopted a landmarking approach to dynamic prediction of all-cause in-hospital mortality over the next 48 hours. We accounted for informative predictor missingness and selected predictors using penalised regression. All data used in this study were obtained from a single UK teaching hospital. We developed the model using 473 consecutive patients with COVID-19 presenting to a UK hospital between 1 March 2020 and 12 September 2020; and temporally validated using data on 1119 patients presenting between 13 September 2020 and 17 March 2021. The primary outcome is all-cause in-hospital mortality within 48 hours of the prediction time. We accounted for the competing risks of discharge from hospital alive and transfer to a tertiary intensive care unit for extracorporeal membrane oxygenation. Our final model includes age, Clinical Frailty Scale score, heart rate, respiratory rate, oxygen saturation/fractional inspired oxygen ratio, white cell count, presence of acidosis (pH <7.35) and interleukin-6. Internal validation achieved an area under the receiver operating characteristic (AUROC) of 0.90 (95% CI 0.87 to 0.93) and temporal validation gave an AUROC of 0.86 (95% CI 0.83 to 0.88). Our model incorporates both static risk factors (eg, age) and evolving clinical and laboratory data, to provide a dynamic risk prediction model that adapts to both sudden and gradual changes in an individual patient's clinical condition. On successful external validation, the model has the potential to be a powerful clinical risk assessment tool. The study is registered as 'researchregistry5464' on the Research Registry (www.researchregistry.com).
To develop a model of clinical leadership that encompasses the specialized technical skills and leadership behaviors exhibited by clinical radiography leaders. During this quantitative study, 432 clinical radiography leaders completed the Clinical Radiography Leadership Survey, which measured the technical skills and leadership behaviors aligned with clinical leadership in radiography. Data analysis included a correlational analysis to examine the relationships between the dimensions measuring technical skills and dimensions measuring leadership behaviors when defining clinical radiography leaders. Participant responses were correlated individually and aggregated by dimension, with Pearson values greater than 0.3 indicating a moderate or strong relationship. The highest interdimensional correlation existed between dimensions 1 (patient care skills) and 2 (technical and radiographic skills) (P = .715), while exhibiting weak correlations to dimensions associated with clinical leadership behaviors. The confirmatory factor analysis revealed that a more global view of clinical leadership behaviors, in addition to patient care and technical skills, informed participants' view of clinical radiography leadership. Participants viewed the technical aspect of their practice as the most salient, and survey items related to professional communication and using sound clinical decision-making skills correlated highly with the leadership behaviors exhibited and valued by clinical radiography leaders. By defining the clinical expertise, technical skills, and commonly practiced leadership behaviors that clinical radiography leaders encompass, other health care professionals will recognize the unique contribution that the radiologic science profession provides.
To develop a tool for measuring competency in conducting health education and to evaluate its psychometric properties in a population of entry-level nurses. Until now, no generic instrument has been developed specifically for measuring competency in health education, which is an essential competency for nurses. Existing scales are either insufficient for psychometric evaluation or are designed specifically for senior nurses. To evaluate curricula and courses designed for entry-level nurses, educators require an instrument for measuring improvement in core competency from baseline to determine whether the minimum level of ability has been achieved. Item development for the survey instrument used for data collection in this study was based on the results of a literature review. The self-evaluated Health Education Competency Scale developed in this study was used to survey 457 nursing students at two nursing schools and 165 clinical nurses at a medical centre in south Taiwan in 2016. The participants were randomly divided into two equal groups. One group was analysed by exploratory factor analysis with varimax rotation, and one group was analysed by confirmatory factor analysis. Factor analysis yielded a four-factor (assessment, pedagogy, motivation and empowerment) solution (18 items) that accounted for 75.9% of the variance. The total scale and subscales had good reliabilities and construct validity coefficients. For measuring competency in entry-level nurses, the Health Education Competency Scale had a good data fit and sound psychometric properties. The proposed scale can be used to assess health education competency for college nursing students and practising nurses. Furthermore, it can provide educators with valuable insight into the minimum competencies required for entry-level nurses to deliver quality health care to clients and can guide them in the practice of client-based teaching.
To develop an instrument to identify the challenges faced by faculty while implementing an integrated curriculum in an undergraduate dentistry program. The study was conducted between September 2020 and October 2021 at the University College of Medicine and Dentistry (UCMD), University of Lahore (UOL). A preliminary questionnaire, developed through literature review and faculty interviews was sent to 10 medical education experts for content validation via the Delphi technique. Content Validity Index (CVI) was calculated for individual items (I-CVI) as well as for the composite scale (S-CVI). A panel agreement of more than 75% was considered as the criterion for the inclusion of items in the questionnaire. Cognitive pretesting of five faculty members was conducted and pilot testing was subsequently done with 27 faculty members. The reliability of the tool was determined by Cronbach's alpha. After the Delphi process, the final Integrated Curriculum Implementation Challenges (ICIC) questionnaire had 42 items. S-CVI was 0.87 and the cut-off value for I-CVI was taken as 0.78 as the criterion for item deletion. Cognitive interviews and pretesting revealed good item interpretation. Cronbach's alpha for this tool was 0.87. ICIC is a useful instrument with good reliability and content validity. It can be used to identify the presence and extent of challenges faced by the faculty while implementing an integrated curriculum.
To develop an internationally standardized and validated tool to assess skill in performing tube shunt surgery. A panel of 6 glaucoma surgeons developed a tool for assessing tube shunt surgery using a modified Dreyfus scale for skill acquisition. The tool was reviewed by a panel of 10 international content experts, and their comments were incorporated into the final rubric. A different panel of 8 international glaucoma specialists independently graded videos of surgical procedures performed by 6 surgeons at various levels of ophthalmic training. Inter-rater reliability for each step in the rubric was calculated. The tube shunt rubric contained 13 steps specific to tube shunt surgery and 7 global indices. The Cronbach α statistic, a measure of internal reliability, ranged from 0.75 to 0.97, indicating strong internal reliability for all 13 steps. The tube shunt assessment tool has face validity, content validity, and interobserver reliability, and can be used to assess tube shunt surgery skills. Further studies are required to determine predictive and construct validity.
To develop and externally validate a 10-year risk prediction model of natural onset of menopause using ready-to-use predictors. Population-based prospective cohort study. Community-dwelling, premenopausal women aged 28 years and older enrolled in the Swiss (CoLaus) and Dutch (PREVEND) study. Incidence of self-reported natural menopause. Based on existing literature, 11 predictors were tested in this study. The CoLaus cohort was used to develop the model by applying the backward-elimination approach and Bayesian Model Averaging. Internal validation was performed by bootstrapping. External validation was performed using data from the PREVEND cohort and recalibrating the baseline survival estimate. C-statistic, calibration slopes, and expected/observed probabilities were calculated as measures of model internal and/or external performances. The final analysis included 750 and 1032 premenopausal women from the CoLaus and the PREVEND cohort, respectively. Among them, 445 (59%) from CoLaus and 387 (38%) from PREVEND experienced menopause over a median follow-up of 10.7 and 9 years, respectively. The final model included age, alcohol consumption, smoking status, education level, and systolic blood pressure. Upon external calibration in the PREVEND cohort, the model exhibited good discrimination, with a C-statistic of 0.888 and an expected/observed probability of 0.82. We present the first internally and externally validated prediction model of natural menopause onset using readily available predictors. Validation of our model to other populations is needed.
To develop and internally validate prediction models for future hospital care utilization in patients with multiple chronic conditions. Retrospective cohort study. A teaching hospital in the Netherlands (542 beds). All adult patients (n = 18.180) who received care at the outpatient clinic in 2017 for two chronic diagnoses or more (including oncological diagnoses) and who returned for hospital care or outpatient clinical care in 2018. Development and validation using a stratified random split-sample (n = 12.120 for development, n = 6.060 for internal validation). ≥2 emergency department visits in 2018, ≥1 hospitalization in 2018 and ≥12 outpatient visits in 2018. Multivariable logistic regression with forward selection. Evaluation of the models' performance showed c-statistics of 0.70 (95% CI 0.69-0.72) for the hospitalization model, 0.72 (95% CI 0.70-0.74) for the ED visits model and 0.76 (95% 0.74-0.77) for the outpatient visits model. With regard to calibration, there was agreement between lower predicted and observed probability for all models, but the models overestimated the probability for patients with higher predicted probabilities. These models showed promising results for further development of prediction models for future healthcare utilization using data from local electronic health records. This could be the first step in developing automated alert systems in electronic health records for identifying patients with multimorbidity with higher risk for high healthcare utilization, who might benefit from a more integrated care approach.
To develop and psychometrically test Character Strengths Use in Diabetes Self-management Scale in people with type 2 diabetes. Cross-sectional design. Based on literature reviews and examination by experts, a 20-item scale was developed and administered to 350 participants with type 2 diabetes who were enrolled from two endocrine clinics by convenience sampling in Taiwan. Item analysis, exploratory factor analysis (EFA), confirmatory factor analysis (CFA), concurrent and predictive validity as well as reliability were used to examine the psychometric characteristics of the scale. Data were collected from November 2021 to March 2022. EFA and CFA supported a 12-item scale with three factors, namely learning proactively, taking on challenges and thinking positively, fit the data well. The total score of the 12-item scale significantly and positively correlated with diabetes-specific quality of life, and significantly and negatively correlated with baseline and 9-month haemoglobin A1c levels. Cronbach's α for overall scale and subscales ranged between .78 and .91. The 12-item Character Strengths Use in Diabetes Self-management Scale demonstrated satisfactory validity and reliability in people with type 2 diabetes. Nurses could apply this new scale to identify the degree of using character strengths in self-management in people with type 2 diabetes; accordingly, character strength-based interventions could be provided to improve self-management in such patients with diabetes. Furthermore, the 12-item Character Strengths Use in Diabetes Self-management Scale has the potential to be used to measure the effectiveness of strength-based interventions in people with Type 2 diabetes. Five patients with type 2 diabetes were invited to take the original 20-item scale to evaluate the clarity, readability and comprehensiveness of the 20 items.
To develop and test an innovative computerized symptom and quality-of-life (QOL) assessment for patients with cancer who are evaluated for and treated with radiation therapy. Descriptive, longitudinal prototype development and cross-sectional clinical data. Department of radiation oncology in an urban, academic medical center. 101 outpatients who were evaluated for radiation therapy, able to communicate in English (or through one of many interpreters available at the University of Washington), and competent to understand the study information and give informed consent. Six clinicians caring for the patients in the sample were enrolled. Iterative prototype development was conducted using a standing focus group of clinicians. The software was developed based on survey markup language and implemented in a wireless, Web-based format. Patient participants completed the computerized assessment prior to consultation with the radiation physician. Graphical output pages with flagged areas of symptom distress or troublesome QOL issues were made available to consulting physicians and nurses. Pain intensity, symptoms, QOL, and demographics. Computerized versions of a 0 to 10 Pain Intensity Numerical Scale (PINS), Symptom Distress Scale, and Short Form-8. Focus group recommendations included clinician priorities of brevity, flexibility, and simplicity for both input interface and output and that the assessment output contain color graphic display. Patient participants included 45 women and 56 men with a mean age of 52.7 years (SD = 13.8). Fewer than half of the participants (40%) reported using a computer on a regular basis (weekly or daily). Completion time averaged 7.8 minutes (SD = 3.7). Moderate to high levels of distress were reported more often for fatigue, pain, and emotional issues than for other symptoms or concerns. Computerized assessment of cancer symptoms and QOL is technically possible and feasible in an ambulatory cancer clinic. A wireless, Web-based system facilitates access to results and data entry and retrieval. The symptom and QOL profiles of these patients new to radiation therapy were comparable to other samples of outpatients with cancer. The ability to capture an easily interpreted illustration of a patients symptom and QOL experience in less than 10 minutes is a potentially useful adjunct to traditional face-to-face interviewing. Ultimately, electronic patient-generated data could produce automated red flags directed to the most appropriate clinicians (e.g., nurse, pain specialist, social worker, nutritionist) for further evaluation. Such system enhancement could greatly facilitate oncology nurses coordination role in caring for complex patients with cancer.
To develop and validate a low-cost homemade bench-top training model to facilitate retrograde intrarenal surgery (RIRS) training. The RIRS training model (G-Model) was developed using a surgical glove and a recycled ureter access sheath. Fifteen participants including 10 residents and 5 urologists were enrolled. Designed training curriculum for residents was carried out. Face validity, content validity, construct validity and criterion validity evaluation of the G-Model were carried out. The global score of face and content validity was 4.15 ± 0.53 and 4.65 ± 0.29, respectively. For construct validity, the overall modified global rating scale (mGRS) score was significantly improved [12.5 (5.25) vs. 24.0 (5.25), p = 0.004], and the total task time was significantly shortened (39.5 ± 4.48 min vs. 24.1 ± 3.81 min, p < 0.001) within residents after G-Model training. The baseline mGRS score and total task time of residents were poorer than those of urologists [12.5 (5.25) vs. 32.0 (1.00), p < 0.001; 39.5 ± 4.48 min vs. 16.0 ± 1.58 min, p < 0.001]. Spearman correlation analysis revealed strong correlations between residents' G-Model and real patient performance. The current study presented a valid low-cost easily accessible RIRS bench-top training model which could facilitate skill acquisition and translate to real-life scenario.