Yoxas/statistical_literacy huggingface dataset:
To develop and validate a prediction model to estimate the risk of Medical Emergency Team (MET) review, within 48 hours of an emergency admission, using information routinely available at the time of hospital admission. Development and validation of a multivariable risk model using prospectively collected data. Transparent Reporting of a multivariable model for Individual Prognosis Or Diagnosis recommendations were followed to develop and report the prediction model. A 560-bed teaching hospital, with a 22-bed ICU and 24-hour Emergency Department in Melbourne, Australia. A total of 45,170 emergency admissions of 30,064 adult patients (≥18 yr), with an inpatient length of stay greater than 24 hours, admitted under acute medical or surgical hospital services between 2015 and 2017. The outcome was MET review within 48 hours of emergency admission. Thirty candidate variables were selected from a routinely collected hospital dataset based on their availability to clinicians at the time of admission. The final model included nine variables: age; comorbid alcohol-related behavioral diagnosis; history of heart failure, chronic obstructive pulmonary disease (COPD), or renal disease; admitted from residential care; Charlson Comorbidity Index score 1 or 2, or 3+; at least one planned and one emergency admission in the last year; and admission diagnosis and one interaction (past history of COPD × admission diagnosis). The discrimination of the model was comparable in the training (C-statistics 0.82; 95% CI, 0.81-0.83) and the validation set (0.81; 0.80-0.83). Calibration was reasonable for training and validation sets. Using only nine predictor variables available to clinicians at the time of admission, the MET-risk model can predict the risk of MET review during the first 48 hours of an emergency admission. Model utility in improving patient outcomes requires further investigation.
To develop and validate a short-form nutrition literacy (NL) assessment tool for Chinese college students based on a 43-item NL measurement scale. To develop and validate short-form NL scale, 1359 college students were surveyed, the data were analyzed using exploratory factor analysis, linear regression analysis, Item analysis, confirmatory factor analysis, and Pearson correlation. The 12-item short-form NL scale (NL-SF12) was developed using factor analysis and regression analysis, which accounted for 96.4% of the variance. The correlation coefficient between the NL-SF12 and NL-43 was 0.969, indicating satisfactory criterion-related validity. The NL-SF12 had a Cronbach's α of 0.890, suggesting strong internal consistency reliability, and content validity index was greater than 0.9, indicating that each domain accurately reflects the connotation of nutrition literacy. The model-data fit and convergent validity of the confirmatory factor analysis results were both good. The NL-SF12 is an effective measurement tool with a good reliability and acceptable validity to assess comprehensively NL for college students, and is applicable to quick, widespread use in population study and practice with low respondent burden.
To develop and validate an instrument to identify the core components of community strategies for mental health, especially mutual aid groups: The Mutual Aid Scale . 135 community strategies leaders participated in the study. The core components are active agency, coping strategies, recognition, and management of emotions, problem-solving strategies, supportive interaction, trust, self-identity construction, and strengthening of social networks. With these components a scale was designed. Content validity was carried out in addition to an exploratory factor analysis. Two dimensions resulted, strengthening of agency capacity and Coping strategies, and the internal consistency of both factors was acceptable, with a Cronbach's alpha of 0.722 and 0.727, respectively. The Kaiser-Meyer-Olkin (KMO) statistic was used with a score of 0.831 and the Barlett Sphericity Test, with a significant value of 265.175. This scale identifies the components of community interventions for mental health and can contribute to a better implementation of these strategies. It also articulates autonomous community processes with strategies developed in health services.
To discuss the application of logic to pattern differentiation for treatment in Traditional Chinese Medicine (TCM). Comparing logic reasoning of syllogism with the logical thinking of TCM pattern differentiation for treatment. TCM logical thinking depends on symbolic and intuitive judgment with abstractive reasoning integrated into the process. Although it lacks quantitative measurement, it pays great attention to the comprehensive analysis of a disease's cause and its development patterns to get insight into the essence of illness. TCM diagnosis reasoning method may lack rigorousness, continuity, systematic induction and deduction, but its logical thinking still can attain its goal following a process with rigorous, regulated and scientific formal logic.
To document the chronic disease risk factors and prevalence rate of family child care professionals. Given that a significant number of young children spend time in family child care (FCC) settings, these environments are an important focus for efforts to improve children's health. Data were collected in fall 2021 from a statewide survey of licensed FCC professionals in one mid-Atlantic state (N=541), using validated questionnaires to assess health status, including chronic diseases like high blood pressure, diabetes, and asthma, as well as nutrition and physical activity. While a majority of respondents reported good overall health and adherence to healthy behaviors like drinking water, eating fruits and vegetables, and engaging in physical activity, a substantial proportion were overweight or have obesity (86.1%), and there were notable rates of high blood pressure (41.1%) and asthma (17.9%). The study found higher diabetes rates among FCC professionals compared to national averages for early childhood education workers, possibly reflecting demographic differences. The results highlight both areas needing support, such as managing chronic disease risks, and areas where FCC professionals excel, like maintaining healthy lifestyle habits. There is a need for targeted support for FCC professionals to manage and prevent chronic diseases, thereby ensuring their wellbeing and enabling them to continue being positive health role models for the children in their care.
To document the process by which healthcare professionals (HCPs) support people living with and beyond hematological cancer and detail how they learned from their personal and clinical experience. Using a narrative approach, we conducted nine semi-structured interviews with HCPs, including nurses, from a specialized care centre who support patients with hematological cancer. Interviews aimed to capture experiential learning gained from their practice. We performed a hybrid inductive/deductive content analysis on data using a framework based on sociological and educational models of experiential learning. Among healthcare professionals, analysis revealed the need to provide care and support that is 'humane' and adapted to each patient. Learning to provide this type of care proved to be challenging. Over the course of their clinical experience, healthcare professionals learned to adapt the support they provided by straddling a boundary between sympathy and empathy. Learning outcomes were associated with personal-professional development among participants. Our findings bring to light an overlooked facet of patient support in the context of cancer care, which is the acquisition of the soft skills required to deliver humanistic care and support. This learning process requires time and involves navigating between the realms of sympathy and empathy. Experiential learning is intertwined with the complexity of the often long-term patient-professional relationship that characterizes hemato-oncology. This unique relationship offers rewards for healthcare professionals on both personal and professional fronts.
To elicit the Aboriginal community's cultural and healthcare needs and views about six prominent and emerging models of care, to inform the development of a new hospital. Cross-sectional qualitative study co-designed and co-implemented by Aboriginal team members. Western Sydney, New South Wales, Australia. Aboriginal and Torres Strait Islander healthcare providers (n=2) and community members (n=18) aged between 21 and 60+ years participated in yarning circles (20 participants; 14 female, 6 male). Handwritten notes from yarning circles were inductively analysed to synthesise the cultural and healthcare needs of providers and community members in relation to a new hospital and six models of care. Three primary themes emerged in relation to future hospitals. These were 'culturally responsive spaces', 'culturally responsive systems' and 'culturally responsive models of care'. Strengths (eg, comfort, reduced waiting time, holistic care), barriers (eg, logistics, accessibility, literacy) and enablers (eg, patient navigator role, communication pathways, streamlined processes) were identified for each of the six models of care. Aboriginal and Torres Strait Islander community members and providers are invested in the co-creation of an innovative, well-integrated hospital that meets the needs of the community. Common themes of respect and recognition, relationships and partnering, and capacity building emerged as important consumer and provider considerations when developing and evaluating care services. Participants supported a range of models citing concerns about accessibility and choice when discussing evidence-based models of care.
To enhance inclusivity and rigor, many funding agencies and journals now mandate the inclusion of females as well as males in biomedical studies. These mandates have enhanced generalizability and created unprecedented opportunities to discover sex differences. However, education in sound methods to consider sex as a subgroup category has lagged behind, resulting in a problematic literature in which study designs, analyses, and interpretations of results are often flawed. Here, we outline best practices for complying with sex-inclusive mandates, both for studies in which sex differences are a primary focus and for those in which they are not. Our recommendations are organized within the "4 Cs of Studying Sex to Strengthen Science: Consideration, Collection, Characterization and Communication," a framework developed by the Office of Research on Women's Health at the National Institutes of Health in the United States. Following these guidelines should help researchers include females and males in their studies while at the same time upholding high standards of rigor.
To enhance understanding in patterns of discordance between clinical and pathological T and N staging in multiple sites of head and neck squamous cell cancer. A retrospective cohort of 580 newly diagnosed and surgically treated head and neck squamous cell carcinoma patients from a single institution over a 10-year period are presented. Clinical and pathologic staging are compared. Notably, 33% of cases had staging discordance. Overall Cohen's kappa agreement was κ = 0.55 (moderate agreement). Highly discordant site stages with κ < 0.45 included: T2 oral cavity, T2 oropharynx, T3 larynx, and N1 lymph node. T2-4 oral cavity cancers were often overstaged, and more than one-third of T3 larynx cancers were understaged. Highly concordant site stages with κ>0.65 included: T1 larynx, T4 oropharynx, N0 lymph node, and N3 lymph node. There exists a quantifiable and, in certain sites, clinically relevant pattern of discordance between clinical and pathologic staging. Tumor board multidisciplinary evaluation can highlight these discrepancies and aide in limiting effects on treatment decisions. However, discordant staging can affect the interpretation and application of prognostication, treatment, and data accuracy. Further investigation is warranted to improve clinical staging accuracy in areas of highest discordance. 3 Laryngoscope, 2024.
To ensure a child's full growth, health, and development during infancy and the early years, adequate nutrition is crucial. A crucial window of opportunity for ensuring children's proper growth and development through adequate eating exists during the first two years of life. According to the evidence of the efficacy of interventions, achieving universal coverage of optimal breastfeeding could prevent 13% of deaths in children under the age of 5 worldwide, and using complementary feeding methods appropriately would lead to an additional 6% decrease in under-five mortality. From several electronic databases, all published, unpublished, and gray literature was extracted and exported into EndNote version X20. For further analysis of the review, the retrieved data from the excel sheet were imported into the statistical software program Stata version. Metanalysis was used to determine the prevalence of MAD, and a random effects model was used to estimate the pooled prevalence of MAD. The DerSimonian-Laird Random effects model (REM) was used to combine the determinant factors from all qualifying papers for the meta-analysis, and the heterogeneity was independently assessed using a χ2 test, Q statistics, and matching I2 statistics. To retrieve the extent of publication bias, funnel plots were scattered and tested for asymmetry and, additionally, Egger's test was computed with the user-written "meta bias" command in Stata (version 11) software. To end, sensitivity analyses with trim and fill were performed. The pooled estimate of the overall prevalence of minimum acceptable diet in 16 studies in Ethiopia was 22% with (95% CI: 16, 28%) with a random effect model. However, eight papers were filled during trim and fill in order to counteract the small study effect. The overall filled pooled estimate was 7.9% with (95%CI: 11, 14.8%). Maternal education (primary and secondary) is 1.714 (95% CI 1.244,2.363) and 2.150(95% CI: 1.449,3.190), respectively, Ages of children with range of 12-17 months (2.158 (95% CI 1. 9,3.006) and 18-23 months 2.948(95% CI: 1.675,5.190)), Nutrition information ((1.883 (95% CI 1.169,3.032)) media exposure (1.778(95% CI: 1.396,2.265), and maternal knowledge (2.449 (95% CI 1.232, 5.027) were significantly associated with MAD. The pooled estimate of the overall prevalence of minimum acceptable diet in 16 studies in Ethiopia were low. Maternal education (primary and secondary), ages of child with range of 12-17 month and 18-23 months, mothers having nutrition information, mothers who have media exposure,and mothers having good knowledge were significantly associated with Minimum acceptable diet. The government, NGO, and other stakeholders should focus on improving Minimum acceptable diet among 6 to 23 months of children through promoting with mass media, focuses on nutrition council during critical contact point in health facility, and doing capacity building for the mothers/caregivers.
To establish a multimodal model for distinguishing benign and malignant breast lesions. Clinical data, mammography, and MRI images (including T2WI, diffusion-weighted images (DWI), apparent diffusion coefficient (ADC), and DCE-MRI images) of 132 benign and breast cancer patients were analyzed retrospectively. The region of interest (ROI) in each image was marked and segmented using MATLAB software. The mammography, T2WI, DWI, ADC, and DCE-MRI models based on the ResNet34 network were trained. Using an integrated learning method, the five models were used as a basic model, and voting methods were used to construct a multimodal model. The dataset was divided into a training set and a prediction set. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the model were calculated. The diagnostic efficacy of each model was analyzed using a receiver operating characteristic curve (ROC) and an area under the curve (AUC). The diagnostic value was determined by the DeLong test with statistically significant differences set at P < 0.05. We evaluated the ability of the model to classify benign and malignant tumors using the test set. The AUC values of the multimodal model, mammography model, T2WI model, DWI model, ADC model and DCE-MRI model were 0.943, 0.645, 0.595, 0.905, 0.900, and 0.865, respectively. The diagnostic ability of the multimodal model was significantly higher compared with that of the mammography and T2WI models. However, compared with the DWI, ADC, and DCE-MRI models, there was no significant difference in the diagnostic ability of these models. Our deep learning model based on multimodal image training has practical value for the diagnosis of benign and malignant breast lesions.
To establish a parentage identification method for Strongylocentrotus intermedius, 15 microsatellite loci and simple sequence repeat sequencing (SSR-seq) technology were used to perform SSR sequencing and typing of the validation population with known pedigree information and the simulation population. Cervus v3.0 was used for gene frequency statistics, simulated analysis, and parentage identification analysis. The results showed that, in validation population, using 15 microsatellite loci, the highest success rate of parent pairs identification was 86%, the highest success rate of female parent identification was 93%, and the highest success rate of male parent identification was 90%. The simulated population was analyzed using 12-15 loci, and the identification rate was up to 90%. In cases where accurate parentage was not achieved, individuals could exhibit genetic similarities with 1-3 male or female parents. Individuals identified as lacking a genetic relationship can be selected as parents to prevent inbreeding. This study shows that parent pairs or single parents of most offspring can be identified successfully using these 15 selected loci. The results lay a foundation for the establishment of a parentage identification method for S. intermedius.
To establish an emergency triage model through the statistical analysis of big data during a particular time period from a hospital information system to improve the accuracy of triage in emergency department (ED). A total of 276,164 patients who visited the Emergency Medicine Department of Beijing Chao-Yang Hospital from 2017 to 2020 were included in this study, including 123,392 men and 152,772 women aged from 14 to 112 years. The baseline characteristics (age and gender) and medical records (patient's condition, body temperature, heart rate, breathing, blood pressure, consciousness, and oxygen saturation) of the patients was collected. The data samples were randomly allocated, with 80% as the training set and 20% as the testing set. The patients were divided into levels I, II, III, and IV in accordance with a four-level triage standard. We selected the effective Extreme Gradient Boosting (XGBoost) algorithm as our emergency classification prediction model. The XGBoost model was applied to simulate the thinking process of triage nurses, and the De Long's test was used to compare the receiver operating characteristic (ROC) curve of different models. The P value was obtained by calculating the variance and covariance of area under the curve (AUC) values of different ROC curves. Level I had 4960 (1.8%) patients, level II had 25,646 (9.29%), level III had 130,664 (47.31%), and level IV had 114,894 (41.6%). The XGBoost model was built following a logic exercise based on the traditional manual pre-inspection and triage results. After verification, the prediction accuracy was 82.57%. The AUC of each disease severity level (levels I, II, III, and IV) was 0.9629, 0.9554, 0.9120, and 0.9296, respectively. The emergency triage prediction model, which achieved a relatively strong accuracy rate, can reduce the work intensity of medical workers and improve their working efficiency.
To establish how advanced practice physiotherapists in the UK working in the musculoskeletal specialty are utilising their independent non-medical prescribing skills. Multi-site, prospective, descriptive observational study. Ethics reference No: ERN_19-0994). The study was conducted by seven advanced practitioners, across seven clinical sites representative of advanced musculoskeletal practice in the UK, between 1st October 2019-March 31, 2020. Advanced physiotherapy practitioner independent prescribers working in a variety of musculoskeletal specialty areas collected data across 5 contexts of musculoskeletal clinical service: first contact practice, primary care, community triage, secondary care orthopaedics, secondary care rheumatology and private practice. Quantitative data were analysed descriptively with qualitative data analysed/synthesised via thematic analysis. Prescribing activity data for n = 2470 patients were collected. Prescribing activity was highest for the treatment of nociceptive pain (51.3%) and inflammation (39.6%). Most prescribing activity occurred in the first 2-6 weeks (34.1%) following onset of condition. Medicines optimisation accounted for most of prescribing activity (18.1%), followed by over-the-counter medication recommendation (15.5%). De-prescribing accounted for 10.8% of all prescribing activity recorded. Qualitative data were synthesised into 4 themes: multimodal physiotherapeutic approach, joint decision making and patient choice, working with complexity, and legal and regulatory restriction. Physiotherapist independent prescribing was used within all health sectors in conjunction with advanced skills in musculoskeletal physiotherapy as part of a multimodal physiotherapeutic approach. Prescribing activity was dictated by the clinicians' clinical reasoning and use of joint decision-making. Prescribing activity for acute back and neuropathic radicular pain was limited secondary to recent reclassification of gabapentin and pregabalin.
To establish whether prevalence and severity of long-COVID symptoms vary by industry and occupation. We used Office for National Statistics COVID-19 Infection Survey (CIS) data (February 2021-April 2022) of working-age participants (16-65 years). Exposures were industry, occupation and major Standard Occupational Classification (SOC) group. Outcomes were self-reported: (1) long-COVID symptoms and (2) reduced function due to long-COVID. Binary (outcome 1) and ordered (outcome 2) logistic regression were used to estimate odds ratios (OR)and prevalence (marginal means). Public facing industries, including teaching and education, social care, healthcare, civil service, retail and transport industries and occupations, had the highest likelihood of long-COVID. By major SOC group, those in caring, leisure and other services (OR 1.44, 95% CIs 1.38 to 1.52) had substantially elevated odds than average. For almost all exposures, the pattern of ORs for long-COVID symptoms followed SARS-CoV-2 infections, except for professional occupations (eg, some healthcare, education, scientific occupations) (infection: OR<1 ; long-COVID: OR>1). The probability of reporting long-COVID for industry ranged from 7.7% (financial services) to 11.6% (teaching and education); whereas the prevalence of reduced function by 'a lot' ranged from 17.1% (arts, entertainment and recreation) to 22%-23% (teaching and education and armed forces) and to 27% (not working). The risk and prevalence of long-COVID differs across industries and occupations. Generally, it appears that likelihood of developing long-COVID symptoms follows likelihood of SARS-CoV-2 infection, except for professional occupations. These findings highlight sectors and occupations where further research is needed to understand the occupational factors resulting in long-COVID.