BackgroundOur aim was to assess the level and socio-demographic correlates of knowledge about rights to healthcare services among children in post-communist Albania in order to inform targeted interventions and policies to promote equitable healthcare access for all children.MethodsAn online survey conducted in Albania in September 2022 included a nationwide representative sample of 7,831 schoolchildren (≈54% girls) aged 12–15 years. A structured and anonymous questionnaire was administered inquiring about children’s knowledge on their rights to healthcare services. Binary logistic regression was used to assess the association of children’s knowledge about their rights to healthcare services with socio-demographic characteristics.ResultsOverall, about 78% of the children had knowledge about their rights to healthcare services. In multivariable adjusted logistic regression models, independent “predictors” of lack of knowledge about rights to healthcare services included male gender (OR = 1.2, 95% CI = 1.1–1.3), younger age (OR = 1.3, 95% CI = 1.1–1.4), pertinence to Roma/Egyptian community (OR = 1.6, 95% CI = 1.1–2.2), and a poor/very poor economic situation (OR = 1.3, 95% CI = 1.0–1.6).ConclusionOur findings indicate a significantly lower level of knowledge about rights to healthcare services among children from low socioeconomic families and especially those pertinent to ethnic minorities such as Roma/Egyptian communities, which can result in limited access to essential health services, increased vulnerability to health disparities, and barriers to receiving appropriate care and advocacy for their health and well-being. Seemingly, gender, ethnicity, and economic status are crucial for children’s knowledge of their healthcare rights because these factors shape their access to information, influence their experiences with healthcare systems, and can drive policy and practice to address disparities and ensure equitable access to health services. Health professionals and policymakers in Albania and elsewhere should be aware of the unmet needs for healthcare services due to lack of awareness to navigate the system particularly among disadvantaged population groups.

The file contains anonymized data on mandibular molar evaluations, including patient demographics, tooth presence, root and canal details, and measurements like the distance to the inferior alveolar nerve canal. (XLSX)

BackgroundDuring the COVID-19 pandemic, non-urgent surgeries were delayed in order to increase the capacity to care for patients with COVID-19. To shed light on the effect of pandemic-related surgical ramp down on the quality of surgical care, this study compared Ontario with Alberta on (1) changes in the proportion of completion and wait time of surgeries with decision-to-treat in a pre-pandemic period compared to those with decision-to-treat in each of the four COVID-19 waves and (2) shifts in healthcare utilization and safety of surgical patients for the same time periods.MethodsA retrospective population-based cohort study was conducted in Ontario on scheduled non-urgent surgeries among adults with decision-to-treat (index dates) between January 1, 2018 and December 31, 2021. Logistic regression was used to examine surgery completion (observed up to December 31, 2021) on the index date period (each COVID-19 wave vs. pre-pandemic). For completed surgeries, median regression was used to assess wait time on the index date period. Descriptive statistics were provided on healthcare utilization and safety indicators among the cohort. Results from regression models and descriptive statistics were then compared with published data from Alberta.ResultsThere were 2,073,688 non-urgent surgeries scheduled for 1,560,265 unique adults in Ontario. Surgeries with an index date in each COVID-19 wave were associated with lower odds of completion compared to the pre-pandemic period, which is in contrast to Alberta where the odds of having surgery completed was not lower during the pandemic than pre-pandemic. Among completed surgeries (91.7%) in Ontario, the median wait time was shorter for surgeries with an index date in waves 2 and 4 than in the pre-pandemic period, while in Alberta the median wait time was shorter for surgeries with index dates in waves 2–4 than pre-pandemic. During the pandemic, Alberta reported a decrease in median intensive care unit (ICU) hours and hospital length of stay for patients relative to pre-pandemic, while Ontario reported an increase in median ICU hours of these patients.ConclusionsThese findings highlight interprovincial differences in surgical care which might be related to COVID-19 policies in each province, healthcare system capacity and patient demographics.

AI-Generated Medical Consent Form Market Outlook

According to our latest research, the global AI-Generated Medical Consent Form market size reached USD 1.14 billion in 2024, reflecting a robust adoption of AI technologies across healthcare documentation. The market is poised to grow at a CAGR of 21.7% from 2025 to 2033, with the forecasted market size expected to reach USD 8.43 billion by 2033. This remarkable growth is primarily driven by the increasing demand for digital transformation in healthcare, the need for enhanced patient data security, and the rising complexity of regulatory compliance worldwide.

One of the most significant growth factors in the AI-Generated Medical Consent Form market is the urgent need for efficiency and accuracy in healthcare documentation. Traditional consent processes are often paper-based, time-consuming, and susceptible to human error, which can lead to legal complications and patient dissatisfaction. AI-generated solutions automate the creation, customization, and management of consent forms, ensuring compliance with evolving medical regulations and standards. These platforms can rapidly adapt to new legal requirements, translate forms into multiple languages, and personalize content based on patient demographics or specific clinical scenarios, thereby improving both workflow efficiency and patient comprehension. The growing emphasis on patient-centric care, coupled with the necessity for error-free documentation, has significantly accelerated the adoption of AI-driven consent solutions globally.

Another key driver for market expansion is the surge in telemedicine and remote healthcare services, especially post-pandemic. As healthcare providers increasingly deliver services beyond traditional clinical settings, the demand for secure, accessible, and legally compliant electronic consent solutions has surged. AI-generated consent forms can be integrated seamlessly into telemedicine platforms, enabling patients to review, understand, and sign documents remotely. This not only facilitates broader access to healthcare but also ensures that providers maintain robust legal and ethical standards. Moreover, AI-powered analytics embedded within these systems can track patient engagement and comprehension, offering insights that help providers tailor communication strategies and further reduce risks associated with consent misunderstandings.

The market is also benefiting from heightened regulatory scrutiny and the proliferation of data privacy laws such as GDPR, HIPAA, and similar frameworks in Asia Pacific and Latin America. These regulations require healthcare providers to ensure explicit, informed consent for the collection and use of patient data. AI-generated consent form solutions are uniquely positioned to address these requirements by automating compliance checks, maintaining detailed audit trails, and updating consent language in real time as regulations evolve. As healthcare organizations face increasing penalties for non-compliance, investment in AI-driven consent technologies has become a strategic imperative. This trend is expected to intensify as global regulators introduce stricter standards for patient data protection and informed consent.

In addition to consent forms, the integration of AI-Generated Patient Intake Chatbot systems is revolutionizing the patient onboarding process. These chatbots are designed to streamline the initial stages of patient interaction by automating the collection of essential information such as medical history, insurance details, and personal data. By reducing the administrative burden on healthcare staff, these AI-driven tools enhance efficiency and accuracy, ensuring that patient data is captured correctly and securely. Furthermore, these chatbots can provide real-time assistance and answer patient queries, improving the overall patient experience and satisfaction. As healthcare providers continue to embrace digital transformation, the adoption of AI-Generated Patient Intake Chatbots is expected to grow, further optimizing patient intake processes and contributing to the overall efficiency of healthcare delivery.

From a regional perspective, North America currently leads the AI-Generated Medical Consent Form market, accounting for the largest share in 2024, driven by early adoption of digital health technologies, strong regulatory frame

Sheet “Prevalence, Incidence”–Prevalent and incident patient numbers. Sheet “Demographics”–Patient counts in age groups, median, mean and standard deviation of age. Sheet “Deaths”–Raw death counts and median age at death. Sheet “Malignancies distribution”–Patient counts with malignancy diagnoses based on 3-digit ICD-10 code. Sheet “CRC epidemiology”–Patient counts with new CRC diagnoses, median age at CRC diagnosis and death, total death counts. Sheet “Survival1”–Survival curve data, OS, UC patients and controls. Sheet “Survival2”–Survival curve data, OS, CRC-UC patients, whole group and age stratified. (XLSX)

THIS RESOURCE IS NO LONGER IN SERVICE, documented on Septemeber 02, 2014. Through a collaborative effort with experts in doctor-elderly patient interaction who participated in the development of ADEPT, a database of approximately 435 audio and video tapes of visits of patients age 65 and older (n=46) to their primary physician was established for testing ADEPT and for access by medical educators and researchers. Data associated with each tape include reason for visit, physician characteristics (age, race, gender), patient characteristics (age, race, gender), companion characteristics (age, race, gender), and length of doctor-patient relationship. Through a collaborative effort with experts in doctor-elderly patient interaction who participated in the development of ADEPT, a database of approximately 435 audio and video tapes of visits of patients age 65 and older (n=46) to their primary physician was established for testing ADEPT and for access by medical educators and researchers. Data associated with each tape include reason for visit, physician characteristics (age, race, gender), patient characteristics (age, race, gender), companion characteristics (age, race, gender), and length of doctor-patient relationship. Patient visits to their primary physician were videotaped at four sites: an academic medical center in the Midwest, an academic medical center in the Southwest, a suburban managed care medical group, and an urban group of physicians in independent practice. Repeat visits between the same doctor and patient were taped for 19 patients resulting in 48 tapes of multiple visits. Patients were recruited in the waiting room for a convenience sample. Before the visit, patients provided demographic data and completed a global satisfaction form. Following the visit, patients completed the SF-36, and the ABIM for patient satisfaction. Two weeks following the visit, patients were contacted by telephone and asked about their understanding, compliance and their utilization of health services over the past year. At twelve months, patients were contacted by telephone for administration of the SF-36, the global satisfaction form, and the utilization of health services survey. Data Availability: Archived at the Saint Louis University School of Medicine Library. Interested researchers and medical educators should contact the PI, Mary Ann Cook, JVCRadiology (at) sbcglobal.net * Dates of Study: 1998-2001 * Study Features: Longitudinal, Anthropometric Measures * Sample Size: 46

BackgroundFamily caregiver’s role can be involving patients with heart failure (HF) in each behavior of self-care such as treatment adherence, and healthy eating, which will \ultimately lead to disease control. This study aimed to investigate family caregivers’ contributions to self-care behaviors among patients with heart failure in Oman.MethodsA descriptive cross-sectional design was used. A convenience sample of 136 family caregivers of patients with HF has completed the family caregivers’ demographics characteristics sheet and the Caregiver Contribution to Self-Care of HF Index2 (CC-SCHFI 2).ResultsCaregivers demonstrated low levels of contribution to patients’ self-care. The mean and (standard deviation) of caregivers’ contribution to maintenance tasks, patients’ ability to perceive symptoms, and to patients’ ability to manage self-care tasks scored 64.12 (SD = 15.70), 66.78 (SD = 14.72).and 52.26 (SD = 15.98) respectively. Education, exercise, and quality of social support were found to have a statistically significant association with caregivers’ contribution to self-care maintenance at a p–value of 0.004, 0.004, 0.004 respectively. While gender, education, marital status, exercise, and quality of social support had statistically significant association with caregivers’ contribution to self-care perception at a p-value of 0.003, 0.002, 0.006,

Background: Oncogenic viruses are a global public health concern that mandates focused research. This retrospective study analyzed clinico-demographic profiles and infection trends of hepatitis B virus (HBV), hepatitis C virus (HCV), human papillomavirus (HPV), and cytomegalovirus (CMV) among laboratory visitors from 2021-2024. Methods: Clinico-demographics (age, gender, specimens, histopathological assessments) and real-time PCR (DNA/RNA copies/ml, genotypes) data were collected and analyzed using SPSS version 17.0. Results: Of the tested patients, 59.22% (706/1192) had HBV infection, 51.45% (160/311) had HCV infection, 16.52% (57/345) had HPV infection, and 34.87% (83/238) had CMV infection. The highest risk ratio for HBV (1.63) or HCV (1.76) infection was among patients aged 20-29, while HPV (1.28) and CMV (2.42) infections were among those aged 20-29 and <1, respectively. CMV was often detected in urine (90.36%, 75/83). HBV infections peaked in 2023. HCV and CMV infection rates d..., Data on demography (e.g., age, gender), clinical findings (e.g., types of specimens, histopathology), and molecular results (viral load, genotype) were retrieved from electronic hospital records (Hospital Information & Management System, ITDOSE INFOSYSTEMS Pvt. Ltd.) for all patients. Uncertain entries were verified with the involved healthcare providers. All variables were entered in Microsoft Excel version 15.0 (2016)., , ## Analysis of clinico-demographic characteristics in PCR-confirmed patients with hepatitis B virus, hepatitis C virus, human papillomavirus, and cytomegalovirus infections at a Nepalese Reference Laboratory

https://doi.org/10.5061/dryad.x3ffbg7wx

Description of the data and file structure

Analysis of Clinico-Demographic Characteristics in PCR-Confirmed Patients with Hepatitis B Virus, Hepatitis C Virus, Human Papillomavirus, and Cytomegalovirus Infections at a Nepalese Reference Laboratory

The dataset is of laboratory-visiting individuals with or without infection due to virus(s).

Dataset comprises of single sheet. The sheet details for demographic information, such as age group and gender of the infected and non-infected individuals; clinical information, including specimen subjected for testing, year of specimen tested, collection, molecular test performed; and histopathological assessments comprising cellular abnormalities. Consider...,

Context

The free-form portions of clinical notes are a significant source of information for research. One path for protecting patient’s privacy is to fully de-identify this information prior to sharing for research purposes . De-identification efforts have focused on known named entities and other known identifier types (names, ages, dates, addresses, ID’s, etc.). However, a note may contain residual “Demographic Traits” (DTs), unique enough to identify the patient when combined with other such facts. While we believe that re-identification is not possible with these demographic traits alone, we hope that giving healthcare organizations the option to remove them will strengthen privacy standards of automatic de-identification systems and bolster their confidence in such systems.

More specifically, this dataset was used to test the performance of our paper ‘Interactive Deep Learning to Detect Demographic Traits in Free-Form Clinical Notes’. We evaluated our pipeline using a subset of the I2b2 2006 and MIMIC-III datasets.

Content

The data contains sentence tagging for MIMIC-III and I2b2 2006 datasets that was used in the paper ‘Interactive Deep Learning to Detect Demographic Traits in Free-Form Clinical Notes’. Every sentence is tagged with its own demographic trait tag (as defined in the "Annotations Guide" file). More formally, the data contains CSV tables each containing rows corresponding to annotated sentences such that every row contains the following example properties: row ID, offset within the note’s text, length and label.

The label mapping (from character to tag) appears in the "Tagged Categories" file. Furthermore, every note in the MIMIC-III dataset contains a unique row-id (appears in a field within the note). In I2b2 2006, every note also contains a unique number, referred to as record-id (which also appears within the note). These features can be found in our attached CSV's under the row_id and record_id columns appropriately. In both cases the offset is defined from the beginning of the note's text.

This EnviroAtlas dataset is a summary of key demographic groups for the EnviroAtlas community. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

BackgroundA standard treatment recommendation for third-line and subsequent treatments for advanced HER2-positive breast cancer is still missing, especially for low HER2 expression. Nevertheless, there is evidence that these patients might benefits from antibody-drug conjugates (ADCs) treatment. Therefore, this study aimed to evaluate the clinical efficacy, safety, and factors affecting efficacy of Disitamab Vedotin (RC48) for treating HER2-positive and HER2-low metastatic breast cancer (MBC) in the real-world setting.MethodsA retrospective study at five clinical sites was conducted in China, enrolling MBC patients treated with RC48 from July 01, 2021 and May 31, 2023. Patient demographics, treatment patterns, and adverse events (AEs) were recorded and analyzed.ResultsA total of 154 patients were included: 104 (67.53%) patients with HER2-positive and 50 (32.47%) patients with HER2-low MBC. The median progression-free survival (mPFS) was 5.06 months. The objective response rate (ORR) and disease control rate (DCR) were 36.36% and 68.83%, respectively. HER2-positive patients exhibited a mPFS of 5.93 and an ORR of 41.35%. In contrast, patients with low-HER2 had a mPFS of 4.28 months and an ORR of 26.00%. The most common AEs included neutropenia (54.55%), increased AST (53.25%), leukopenia (51.95%), and fatigue (43.51%), mostly graded mild to moderate (grade 1-2).ConclusionsThis extensive study in China demonstrated that RC48 has excellent therapeutic potential for both HER2-positive and HER2-low MBC with a favorable safety profile. The study also suggests that combination therapy significantly boosts efficacy beyond monotherapy, indicating a promising avenue for future ADC development.

In this study we explore the relationship between faculty demographics such as gender, academic rank, and field, and the presence of Identity Safety Cues (ISCs) in undergraduate course syllabi. ISCs, such as the inclusion of instructor pronouns, inclusivity statements, and materials authored by women and gender minority scholars, are increasingly seen as indicators of inclusive teaching practices. Drawing from an original dataset of 163 syllabi from introductory undergraduate courses at Worcester Polytechnic Institute, a STEM-focused institution in the United States of America, we examine how these cues are used by faculty across different fields, gender, and rank. We employ a combination of descriptive and predictive statistics methods to investigate the influence of faculty demographics on syllabi design. Our findings reveal that women faculty across all disciplines are more likely to include ISCs compared to their male counterparts. Field also plays a substantial role in syllabi design, with faculty in the Humanities & Arts including ISCs much more frequently than those in STEM and the Social Sciences. The implications of these findings suggest a need for targeted faculty professional development and mentorship to promote better inclusive pedagogy in STEM education.

Office of Head Start (OHS) Program Fact Sheet provides information on demographics, state allocations, program statistics, and program enrollment history.

Gleevec Market size was valued at USD 344 Million in 2023 and is projected to reach USD 644.7 Million by 2031, growing at a CAGR of 8.29% during the forecast period 2024-2031.

Global Gleevec Market Drivers The market drivers for the Gleevec Market can be influenced by various factors. These may include:

Increasing Cancer Incidence: The rising incidence of cancer, particularly chronic myeloid leukemia (CML), is a key driver for the Gleevec market. As more individuals are diagnosed with cancer, the demand for effective treatment options, such as Gleevec, continues to grow. According to global cancer statistics, the prevalence of CML and other cancers has been on the rise due to factors like aging populations and lifestyle choices. This growing patient population increases the need for targeted therapies. Consequently, pharmaceutical companies are focusing on Gleevec’s market expansion, making it a critical component of cancer treatment regimens worldwide, leading to sustained revenue streams for manufacturers. Patent Expiration and Generic Competition: The expiration of Gleevec’s patent created a significant shift in the market dynamics. Once the patent lapsed, generic versions of imatinib flooded the market, resulting in decreased prices and accessibility for patients. This influx of generics intensified competition, causing brand-name Gleevec to lose market share. However, while this presents challenges, it also drives market efficiency, providing patients with more affordable treatment options. Generics have diversified the market while ensuring that more individuals receive life-saving therapies. As healthcare systems adapt, the competition encourages continuous innovation within the pharmaceutical industry to capture unmet medical needs in cancer therapies.

Global Gleevec Market Restraints Several factors can act as restraints or challenges for the Gleevec Market. These may include:

High Cost of Medication: The Gleevec market faces significant restraint due to the high cost of the medication. With a price tag often exceeding several thousand dollars monthly, affordability becomes a barrier for many patients, particularly in low- and middle-income countries. High out-of-pocket expenses may deter patients from initiating or continuing treatment, leading to reduced market penetration. As healthcare systems around the world grapple with rising pharmaceutical costs, the pressure to provide affordable treatment options intensifies. Furthermore, insurance coverage discrepancies create disparities in access, hindering Gleevec’s market growth despite its proven efficacy against certain cancers. Generic Competition: The entry of generic alternatives poses a substantial restraint to the Gleevec market. Once the patent for Gleevec expired, several pharmaceutical companies began to produce generic versions, offering lower-priced alternatives that challenge Novartis’ original product. This increased competition compresses profit margins and may shift consumer choice towards more affordable options. Additionally, healthcare providers may favor generics under cost-saving initiatives, further limiting Gleevec’s market share. As regulatory bodies encourage the use of generics to improve access to life-saving medications, Gleevec faces ongoing pressure from this influx of competitive pricing, impacting future sales.

Background: Multidrug-resistant (MDR) bacteria cause infections with higher risks of morbidity, mortality, and financial burden, emphasizing the importance of understanding pathogen-specific resistance patterns for effective treatment and resistance management. Therefore, this retrospective cohort study examined the prevalence, causes, and trends of antimicrobial resistance in bacterial infections in a neurosurgical hospital in Nepal. Method: We analyzed the demographics, bacteriological profiles, and antimicrobial susceptibility results of patients who visited a neurosurgical hospital in Kathmandu, Nepal, between January 2014 and January 2024, using SPSS, version 17.00. Results: Among 4,758 patients, 465 (9.77%) had infections caused by 571 bacteria. Of them, 435 (93.55%) patients had urinary tract infections, 89 (19.14%) had bloodstream infections, and 31 (6.67%) had respiratory tract infections. Klebsiella pneumoniae (n=172, 30.12%) was the predominant bacteria. Resistance rates for ..., In the patient information sheet, outcome variables [bacterial pathogens and viral-bacterial coinfections (simultaneous occurrences)] and predictor variables (patient demographics, time frame, specimen type, type of bacterial isolate(s), and antimicrobial susceptibility patterns) were collected from the hospital records. The data were anonymized to ensure patient confidentiality. Data was entered and managed using Microsoft Excel, version 13.0, and analyzed using Statistical Package for Social Sciences (SPSS), version 17.0. Descriptive data were analyzed in terms of frequency and percentage. Quantitative data were reported as mean, median, and interquartile range (IQR). Qualitative variables were analyzed using the Chi-square test, while quantitative variables were analyzed using the independent student t-test, with statistical significance determined at a p-value of <0.05 within a 95% confidence interval (CI)., , # A decade-long analysis of trends in antimicrobial resistance at a neurosurgical hospital in Kathmandu, Nepal

https://doi.org/10.5061/dryad.zpc866thj

Description of the data and file structure

A decade-long analysis of trends in antimicrobial resistance at a neurosurgical hospital in Kathmandu, Nepal

The dataset is of hospital-visiting individuals who were with and without bacterial infections.

The dataset consists of two sheets. The first sheet details clinico-demographic information, such as patient age group, gender, specimen type, and bacterial infection status. The exact age is removed and categorized as an age group to anonymize the data. The second sheet details the year, bacterial species, and antimicrobial susceptibility patterns. Given that the patient identification number and specimen number are classified as human subject data and require anonymization, these identifiers have been removed from the dataset.

Not availabl...