Yoxas/statistical_literacy huggingface dataset:
Radiological diagnosis is based on subjective judgment by radiologists. The reasoning behind this process is difficult to document and share, which is a major obstacle in adopting evidence-based medicine in radiology. We report our attempt to use a comprehensive brain parcellation tool to systematically capture image features and use them to record, search, and evaluate anatomical phenotypes. Anatomical images (T1-weighted MRI) were converted to a standardized index by using a high-dimensional image transformation method followed by atlas-based parcellation of the entire brain. We investigated how the indexed anatomical data captured the anatomical features of healthy controls and a population with Primary Progressive Aphasia (PPA). PPA was chosen because patients have apparent atrophy at different degrees and locations, thus the automated quantitative results can be compared with trained clinicians' qualitative evaluations. We explored and tested the power of individual classifications and of performing a search for images with similar anatomical features in a database using partial least squares-discriminant analysis (PLS-DA) and principal component analysis (PCA). The agreement between the automated z-score and the averaged visual scores for atrophy (r = 0.8) was virtually the same as the inter-evaluator agreement. The PCA plot distribution correlated with the anatomical phenotypes and the PLS-DA resulted in a model with an accuracy of 88% for distinguishing PPA variants. The quantitative indices captured the main anatomical features. The indexing of image data has a potential to be an effective, comprehensive, and easily translatable tool for clinical practice, providing new opportunities to mine clinical databases for medical decision support.
Radiology and pathology, though distinct fields within medical education, share a common foundation in their essential roles for accurate diagnosis and understanding of diseases. While pathology, particularly histopathology, has long been integrated into preclinical medical education in the United States, radiology education has traditionally been less emphasized. This paper examines the historical development of histopathology training in medical education and its central role, contrasting it with the comparatively peripheral position of radiology education. We explore the historical context of medical education in the United States, tracing the integration of histopathology following the Flexner Report of 1910. In contrast, radiology, emerging later as a specialized field, has faced challenges in achieving comparable integration into medical curricula. Despite the increasing importance of medical imaging in diagnosis and treatment, radiology education remains variable and often lacking in standardization across medical schools. We highlight the need for greater emphasis on radiology education to better prepare medical students for modern clinical practice, where medical imaging plays an increasingly pivotal role. A call for a comprehensive assessment of radiology education and advocacy for its integration into preclinical curricula is made, emphasizing the importance of collaboration between the radiology profession and accrediting bodies to ensure competence in imaging across medical specialties. As medical imaging continues to advance and become more integral to healthcare, it is imperative that medical education reflects this evolution by establishing radiology as a fundamental component of preclinical training.
Radiomics is a rapidly growing field used to leverage medical radiological images by extracting quantitative features. These are supposed to characterize a patient's phenotype, and when combined with artificial intelligence techniques, to improve the accuracy of diagnostic models and clinical outcome prediction. This review aims at examining the application areas of artificial intelligence-based radiomics (AI-based radiomics) for the management of head and neck cancer (HNC). It further explores the workflow of AI-based radiomics for personalized and precision oncology in HNC. Finally, it examines the current challenges of AI-based radiomics in daily clinical oncology and offers possible solutions to these challenges. Comprehensive electronic databases (PubMed, Medline via Ovid, Scopus, Web of Science, CINAHL, and Cochrane Library) were searched following the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. The quality of included studies and their risk of biases were evaluated using the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD)and Prediction Model Risk of Bias Assessment Tool (PROBAST). Out of the 659 search hits retrieved, 45 fulfilled the inclusion criteria. Our review revealed that the application of AI-based radiomics model as an ancillary tool for improved decision-making in HNC management includes radiomics-based cancer diagnosis and radiomics-based cancer prognosis. The radiomics-based cancer diagnosis includes tumor staging, tumor grading, and classification of malignant and benign tumors. Similarly, radiomics-based cancer prognosis includes prediction for treatment response, recurrence, metastasis, and survival. In addition, the challenges in the implementation of these models for clinical evaluations include data imbalance, feature engineering (extraction and selection), model generalizability, multi-modal fusion, and model interpretability. Considering the highly subjective and interobserver variability that is peculiar to the interpretation of medical images by expert clinicians, AI-based radiomics seeks to offer potentially useful quantitative information, which is not visible to the human eye or unintentionally often remain ignored during clinical imaging practice. By enabling the extraction of this type of information, AI-based radiomics has the potential to revolutionize HNC oncology, providing a platform for more personalized, higher quality, and cost-effective care for HNC patients.
Radiotherapy after breast-conserving therapy is a standard postoperative treatment of breast cancer, which can be carried out with a variety of irradiation techniques. The treatment planning must take into consideration detrimental effects on the neighbouring organs at risk-the lung, the heart, and the contralateral breast, which can include both short- and long-term effects represented by the normal tissue complication probability and secondary cancer risk. In this planning study, we investigate intensity-modulated (IMRT) and three-dimensional conformal (3D-CRT) radiotherapy techniques including sequential or simultaneously integrated boosts as well as interstitial multicatheter brachytherapy boost techniques of 38 patients with breast-conserving surgery retrospectively. We furthermore develop a 3D-printed breast phantom add-on to allow for catheter placement and to measure the out-of-field dose using thermoluminescent dosimeters placed inside an anthropomorphic phantom. Finally, we estimate normal tissue complication probabilities using the Lyman-Kutcher-Burman model and secondary cancer risks using the linear non-threshold model (out-of-field) and the model by Schneider et al. (in-field). The results depend on the combination of primary whole-breast irradiation and boost technique. The normal tissue complication probabilities for various endpoints are of the following order: 1%-2% (symptomatic pneumonitis, ipsilateral lung), 2%-3% (symptomatic pneumonitis, whole lung), and 1%-2% (radiation pneumonitis grade ≥ 2, whole lung). The additional relative risk of ischemic heart disease ranges from +25% to +35%. In-field secondary cancer risk of the ipsilateral lung in left-sided treatment is around 50 per 10,000 person-years for 20 years after exposure at age 55. Out-of-field estimation of secondary cancer risk results in approximately 5 per 10,000 person-years each for the contralateral lung and breast. In general, 3D-CRT shows the best risk reduction in contrast to IMRT. Regarding the boost concepts, brachytherapy is the most effective method in order to minimise normal tissue complication probability and secondary cancer risk compared to teletherapy boost concepts. Hence, the 3D-CRT technique in combination with an interstitial multicatheter brachytherapy boost is most suitable in terms of risk avoidance for treating breast cancer with techniques including boost concepts.
Radiotherapy is recommended for the treatment of brain tumors such as glioblastoma (GBM) and brain metastases. Various curative and palliative scenarios suggest improved local-regional control. Although the underlying mechanisms are not yet clear, additional therapeutic effects have been described, including proximity and abscopal reactions at the treatment site. Clinical and preclinical data suggest that the immune system plays an essential role in regulating the non-targeted effects of radiotherapy for GBM. This article reviews current biological mechanisms for regulating the non-targeted effects caused by external and internal radiotherapy, and how they might be applied in a clinical context. Optimization of therapeutic regimens requires assessment of the complexity of the host immune system on the activity of immunosuppressive or immunostimulatory cells, such as glioma-associated macrophages and microglia. This article also discusses recent preclinical models adapted to post-radiotherapy responses. This narrative review explores and discusses the current status of immune responses both locally via the "bystander effect" and remotely via the "abscopal effect". Preclinical and clinical observations demonstrate that unirradiated cells, near or far from the irradiation site, can control the tumor response. Nevertheless, previous studies do not address the problem in its global context, and present gaps regarding the link between the role of the immune system in the control of non-targeted effects for different types of radiotherapy and different fractionation schemes applied to GBM. This narrative synthesis of the scientific literature should help to update and critique available preclinical and medical knowledge. Indirectly, it will help formulate new research projects based on the synthesis and interpretation of results from a non-systematic selection of published studies.
Random matrix theory, particularly using matrices akin to the Wishart ensemble, has proven successful in elucidating the thermodynamic characteristics of critical behavior in spin systems across varying interaction ranges. This paper explores the applicability of such methods in investigating critical phenomena and the crossover to tricritical points within the Blume-Capel model. Through an analysis of eigenvalue mean, dispersion, and extrema statistics, we demonstrate the efficacy of these spectral techniques in characterizing critical points in both two and three dimensions. Crucially, we propose a significant modification to this spectral approach, which emerges as a versatile tool for studying critical phenomena. Unlike traditional methods that eschew diagonalization, our method excels in handling short timescales and small system sizes, widening the scope of inquiry into critical behavior.
Randomized clinical trials (RCTs) are the gold standard for assessing treatment effectiveness; however, they have been criticized for generalizability issues such as how well trial participants represent those who receive the treatments in clinical practice. We assessed the representativeness of participants from eight RCTs for chronic spine pain in the U.S., which were used for an individual participant data meta-analysis on the cost-effectiveness of spinal manipulation for spine pain. In these clinical trials, spinal manipulation was performed by chiropractors. We conducted a retrospective secondary analysis of RCT data to compare trial participants' socio-demographic characteristics, clinical features, and health outcomes to a representative sample of (a) U.S. adults with chronic spine pain and (b) U.S. adults with chronic spine pain receiving chiropractic care, using secondary data from the National Health Interview Survey (NHIS) and Medical Expenditure Panel Survey (MEPS). We assessed differences between trial and U.S. spine populations using independent t-tests for means and z-tests for proportions, accounting for the complex multi-stage survey design of the NHIS and MEPS. We found the clinical trials had an under-representation of individuals from health disparity populations with lower percentages of racial and ethnic minority groups (Black/African American 7% lower, Hispanic 8% lower), less educated (No high school degree 19% lower, high school degree 11% lower), and unemployed adults (25% lower) with worse health outcomes (physical health scores 2.5 lower and mental health scores 5.3 lower using the SF-12/36) relative to the U.S. population with spine pain. While the odds of chiropractic use in the U.S. are lower for individuals from health disparity populations, the trials also under-represented these populations relative to U.S. adults with chronic spine pain who visit a chiropractor. Health disparity populations are not well represented in spine pain clinical trials. Embracing key community-based approaches, which have shown promise for increasing participation of underserved communities, is needed.
Randomized controlled trials (RCTs) and real-world evidence (RWE) studies are crucial and complementary in generating clinical evidence. RCTs provide controlled settings to validate the clinical effect of specific drugs or medical devices, while RWE integrates extrinsic factors, encompassing external influences affecting real-world scenarios, thus challenging RCT results in practical applications. In this study, we explore the impact of extrinsic factors on RWE outcomes, focusing on "dark data," which refers to data collected but not used or excluded from the analyses. Dark data can arise in many ways during research process, from selecting study samples to data collection and analysis. However, even unused or unanalyzed dark data hold potential insights, providing a comprehensive view of clinical contexts. Extrinsic factors lead to divergent RWE outcomes that could differ from RCTs beyond statistical correction's scope. Two main types of dark data exist: "known-unknown" and "unknown-unknown." The distinction between these dark data types highlights RWE's complexity. The transformation of unknown into known depends on data literacy-powerful utilization capabilities that can be interpreted based on medical expertise. Shifting the focus to excluded subjects or unused data in real-world contexts reveals unexplored potential. Understanding the significance of dark data is vital in reflecting the complexity of clinical settings. Connecting RCTs and RWEs requires medical data literacy, enabling clinicians to decipher meaningful insights. In the big data and artificial intelligence era, medical staff must navigate data complexities while promoting the core role of medicine. Prepared clinicians will lead this transformative journey, ensuring data value shapes the medical landscape.
Randomized experiments involving education interventions are typically implemented as cluster randomized trials, with schools serving as clusters. To design such a study, it is critical to understand the degree to which learning outcomes vary between versus within clusters (schools), specifically the intraclass correlation coefficient. It is also helpful to anticipate the benefits, in terms of statistical power, of collecting household data, testing students at baseline, or relying on administrative data on previous cohorts from the same school. We use data from multiple cluster-randomized trials in four Latin American countries to provide information on the intraclass correlations in early grade literacy outcomes. We also describe the proportion of variance explained by different types of covariates. These parameters will help future researchers conduct statistical power analysis, estimate the required sample size, and determine the necessity of collecting different types of baseline data such as child assessments, administrative data at the school level, or household surveys.
Ranking theory is a formal epistemology that has been developed in over 600 pages in Spohn's recent book The Laws of Belief, which aims to provide a normative account of the dynamics of beliefs that presents an alternative to current probabilistic approaches. It has long been received in the AI community, but it has not yet found application in experimental psychology. The purpose of this paper is to derive clear, quantitative predictions by exploiting a parallel between ranking theory and a statistical model called logistic regression. This approach is illustrated by the development of a model for the conditional inference task using Spohn's (2013) ranking theoretic approach to conditionals.
Rapid advancements in technology refine our understanding of intricate biological processes, but a crucial emphasis remains on understanding the assumptions and sources of uncertainty underlying biological measurements. This is particularly critical in cell signaling research, where a quantitative understanding of the fundamental mechanisms governing these transient events is essential for drug development, given their importance in both homeostatic and pathogenic processes. Western blotting, a technique developed decades ago, remains an indispensable tool for investigating cell signaling, protein expression, and protein-protein interactions. While improvements in statistical analysis and methodology reporting have undoubtedly enhanced data quality, understanding the underlying assumptions and limitations of visual inspection in Western blotting can provide valuable additional information for evaluating experimental conclusions. Using the example of agonist-induced receptor post-translational modification, we highlight the theoretical and experimental assumptions associated with Western blotting and demonstrate how raw blot data can offer clues to experimental variability that may not be fully captured by statistical analyses and reported methodologies. This article is not intended as a comprehensive technical review of Western blotting. Instead, we leverage an illustrative example to demonstrate how assumptions about experimental design and data normalization can be revealed within raw data and subsequently influence data interpretation.
Rapid Cycle Deliberate Practice (RCDP) has gained prominence in recent years as an innovative teaching method in simulation-based training for adult and pediatric emergency medical skills. However, its application in the training of forceps delivery skills among obstetrics and gynecology residents remains unexplored. This study aimed to assess the impact of RCDP in this domain. Conducted in March 2021, this randomized controlled study involved 60 second-year obstetrics and gynecology residents undergoing standardized training. Participants were randomly assigned to the RCDP group or the traditional teaching method (TTM) group, each comprising 30 residents. The RCDP group followed the RCDP practice mode, while the TTM group adhered to conventional simulation teaching. Post-training assessment of operational proficiency was conducted immediately and after one year. Independent operational confidence and training satisfaction were evaluated through questionnaire surveys and the Satisfaction with Simulation Experience (SSE) scale. Data analysis utilized SPSS 23.0. The RCDP group displayed significantly higher immediate post-training forceps operation scores compared to the TTM group (92.00 [range: 90.00-94.00] vs. 88.00 [range: 86.75-92.00]; z = 3.79; p < .001). However, no significant difference emerged in forceps operation scores after one year (86.00 [range: 85.00-88.00] vs. 85.50 [range: 84.00-88.25]; z = 0.54; p = .59). The RCDP group exhibited notable performance improvement over the TTM group (z = 3.49; p < .001). Independent operation confidence showed no significant discrepancy (p > .05). Importantly, the RCDP group reported higher satisfaction scores, particularly in the Debriefing and Reflection subscale (44.00 [range: 43.00-45.00] vs. 41.00 [range: 41.50-43.00]; z = 5.24; p < .001), contributing to an overall superior SSE score (z = 4.74; p < .001). RCDP exhibits immediate efficacy in elevating forceps delivery skills among residents. However, sustained skill enhancement necessitates innovative approaches, while RCDP's value lies in tailored feedback and reflection for enriched medical education.
Rapid economic growth has widened the gap between the rich and the poor, contributing to inequalities in socioeconomic status (SES) in Indonesia and possibly inequalities in health care. Here, we aimed to assess the potential association between SES and stroke severity in Indonesia, one of the largest low- and middle-income countries. Patients diagnosed with stroke at National Brain Centre (NBC) Hospital, Jakarta, Indonesia, in 2020 were included in the study. SES was measured based on marital status, occupation, education level, source of payment, and hospitalized class with smoking status and sex as confounder. Stroke severity was classified based on the National Institutes of Health Stroke Scale score into minor stroke (Adams et al., 1993, Amarenco et al., 2014, Andersen and Olsen, 2018, Austin and Steyerberg, 2017) and moderate to severe stroke (>4). A total of 2,443 patients with moderate to severe stroke (58%) were analyzed. Currently employed patients had a lower adjusted OR (aOR) of 0.65 (95% confidence interval [CI], 0.51-0.83) than unemployed patients. Patients with the highest education level, at least a diploma degree, had a lower aOR of 0.67 (95% CI, 0.49-0.92) than those with an elementary or no education. Our findings showed that patients with a higher SES had a lower risk of more severe stroke than those with a lower SES. Hence, we must focus on improving SES as part of stroke management.
Rapid growth in pediatric hospital medicine (PHM) fellowships has occurred, yielding many new program directors (PDs). Characteristics of PDs have potential implications on the field. To describe characteristics (demographic, educational) and scholarly interests of PHM fellowship PDs. We developed and distributed a 15-question, cross-sectional national survey to the PHM PDs listserv. Questions were pilot tested. The survey was open for 4 weeks with weekly reminders. Responses were summarized using descriptive statistics. Fifty-six current fellowship leaders (40 PDs, 16 associate PDs [APDs]) responded, including at least 1 from 43 of 59 active PHM fellowship programs (73%). Most respondents identified as female (71%) and ≤50 years old (80%). Four (7%, n = 2 PD, 2 APD) leaders identified as underrepresented in medicine. About half (n = 31, 55.4%) completed a fellowship themselves (APDs > PDs; 87.5% vs 42.5%), and 53.5% (n = 30) had advanced nonmedical degrees (eg, Master of Science, Doctor of Philosophy; APDs > PDs; 62% vs 45%). Most leaders (59%, n = 33) chose multiple domains when asked to select a "primary domain of personal scholarship." Education was the most frequently selected (n = 37), followed by quality improvement (n = 29) and then clinical research (n = 19). This survey confirms a high percentage of women as PHM fellowship leaders and highlights the need to increase diversity. Less than half of senior PDs completed a fellowship in any specialty. Leaders report interest in multiple domains of scholarship; few focus solely on clinical research.