Child development: prenatal to early childhood.
Explore the source record for details and available documents.
Animals↗
SEARCH · Search PubMed
Results for “Disability Discrimination”
Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Autism ableism seen through research abstract contents: A mixed-methods analysis of language in NIH-funded genetic and genomic autism research.
In recent years, genetic and genomic autism research has come under increasing scrutiny, moving to the center of debates about ableism, neurodiversity, autism acceptance, and the future of research and care. At the same time, both autism research and genetics and genomics research have, as fields, begun to reckon with the significance of the language researchers use in the course of their work and the harmful ideas that may thereby be reinforced. Although the language of research cannot be assumed to straightforwardly correspond to individual researchers' beliefs, the presence of widespread ableist language may indicate structural and institutionalized ableism, including ableist assumptions at the foundations of research. We conducted a mixed-methods analysis of 166 genetic and genomic autism research projects funded by the US National Institutes of Health, in order to understand the prevalence of potentially ableist discourse, language, and stigmatizing language about autistic people. We found that such discourse and language was ubiquitous across our sample, including a discourse of prevention. This study lends empirical evidence to current debates about language in autism research. Evaluating language can prompt researchers and institutions to reflect on how they conceptualize, design, discuss, and pursue their work.Lay abstractGenetic research about autism is controversial. Researchers are starting to think more carefully about the words they use to talk about autism and the way they do their research. Past research has found that researchers sometimes write about autism in ableist ways. This means that they write about autistic people as though they are less important than nonautistic people. We looked at the way genetics researchers have written about autism in the paperwork for their research. We found that they often write about autistic people in an ableist way. We think that researchers should think carefully about the way they write about autistic people, and how they plan and do their research.
Humans↗
IMPROVE kidney care: perspectives from marginalised people with CKD and risk factors for CKD on access to, and experience of, kidney care services: a cross-sector collaborative exploration, employing qualitative approaches.
BACKGROUND: Access to, and experience of, chronic kidney disease (CKD) care is inequitable-with barriers to accessing quality care for marginalised groups. We conducted an exploratory study employing qualitative approaches to understand the factors that influence access to, and experience of, healthcare services for marginalised people with CKD and at risk of CKD. METHODS: An exploratory study employing qualitative approaches was conducted as a cross-sector collaboration between kidney care services and an activist, antiracist community-based research and social justice organisation (Mabadiliko Community Interest Company (CIC)). Two groups were recruited: 1) those with risk factors for CKD or early-stage CKD, and 2) people who presented late to kidney care services. Semi-structured interviews were co-designed with people with lived experience and conducted by Mabadiliko CIC. Thematic analysis was undertaken, with themes refined by participants. RESULTS: Twenty interviews were undertaken with a diverse cohort of participants. Knowledge and awareness of CKD was limited, and compounded by a lack of delivery of accessible, culturally congruent information. Significant barriers to accessing kidney care exist for marginalised people, including people who are from global majority ethnic backgrounds, Disabled people, and/or people experiencing material hardship. These barriers are compounded by interpersonal discrimination and paternalistic power dynamics within healthcare interactions. CONCLUSION: This study captures the experiences of marginalised people at different stages of their journey with CKD, in accessing and engaging with kidney care services. Participants faced a complex array of challenges, highlighting opportunities for multi-level intervention. We outline recommendations to address these issues, co-developed with participants.
chronic kidney disease↗
EEG asymmetry in educationally handicapped children.
Bilaterally homologous parietal and temporal EEGs were recorded from two groups of educationally handicapped children while the subjects (Ss) rested with eyes open or closed or performed in simple tasks designed to differentially activate the two hemispheres (reading by Ss, reading to Ss, draw a picture). A 'dyslexic' but not dysphasic group showed a reversal of theta asymmetry from eyes closed to eyes open (L less than R to L greater than R). This difference between groups was reflected most at the parietal placement. Theta in the parietal lead changed in accordance with expectations from reading to drawing (reduction of the R/L ratio), but that pattern did not occur at the temporal lead in 'dyslexics'. A discriminant analysis on theta power correctly classified 20 of the 22 children.
Adolescent↗
Maladjusted children and the school health services. Development of a screening test for routine use.
Maladjustment, with social and mental dysfunction, is one of the most prevalent health problems threatening children in many countries. A method for the early identificantion of maladjusted schoolchildren is described and evaluated. The method involves the completion of a questionnaire for each child by the teacher and the calculation of a test score based on a set of weighting factors assigned to each question. The factors are determined by discriminant analysis. For a proper evaluation of the method an independent clinical assessment of maladjustment among 524 school children was performed, making calculations of specificity and sensitivity possible.
Child↗
Home observation for measurement of the environment: a validation study of screening efficiency.
Home environments of 91 6-month-old infants were assessed with the Home Observation for Measurement of the Environment (HOME) Inventory. Multiple discriminant functions composed of the six subscale scores from the HOME Inventory were used to predict whether a child would be low IQ (below 70), low average (70 to 89), or average to superior (90 and above) at age 3 years. The mean vector of Home inventory subscales for the three IQ groups was significantly different (p less than .01). Significant univariate effects were observed for three HOME Inventory subscales: organization of the physical and temporal environment, provision of appropriate play materials, and maternal involvement with child. The discriminant function of HOME Inventory subscale scores correctly predicted 71 percent of all children who scored below 70 IQ. Results attest to the usefulness of the HOME Inventory in a comprehensive program of screening for developmental delay.
Child, Preschool↗
Comparison of the predictive performance of systemic immune-inflammation index and neutrophil-to-lymphocyte ratio for three-month poor functional outcome in ischemic stroke: a systematic review and meta-analysis.
INTRODUCTION: Ischemic stroke (IS) is a leading cause of global mortality and disability. Early and accurate prognosis is crucial for patient management. The neutrophil-to-lymphocyte ratio (NLR) and systemic immune-inflammation index (SII) are emerging inflammatory biomarkers; however, their relative predictive value for three-month poor functional outcome (modified Rankin Scale [mRS] > 2) remains uncertain. METHODS: We systematically searched PubMed, Embase, Web of Science, and the Cochrane Library up to 20 July 2025, adhering to PRISMA guidelines. Observational studies reporting the association of SII or NLR with three-month poor outcome were included. Study quality was evaluated using the Newcastle-Ottawa Scale. Area under the curve (AUC), odds ratios (OR), and standardized mean differences (SMD) were pooled using random-effects models in Stata 16.0. RESULTS: Twenty-one studies involving 7520 IS patients were analysed. NLR demonstrated marginally superior discriminative ability compared to SII (AUC 0.71, 95% CI: 0.67-0.76 vs. 0.68, 95% CI: 0.64-0.71), though this difference was not statistically significant. Elevated NLR was significantly associated with poor outcome (OR = 1.26, 95% CI: 1.17-1.37, p < .001), whereas SII was not (OR = 1.00, 95% CI: 1.00-1.00, p = .384). Both markers showed moderate effect sizes (SMD: NLR = 0.69, SII = 0.72; p < .001). NLR performed better in non-intervention and Chinese subgroups, while SII exhibited consistent AUC values across treatment and ethnic subgroups. CONCLUSION: NLR and SII are accessible prognostic markers in IS. NLR demonstrates superior accuracy and a significant association with poor outcome, while SII shows greater stability across patient subgroups. Both may assist in risk stratification, in resource-limited settings.
Humans↗
The role of artificial intelligence in the diagnosis and prognosis of traumatic brain injury based on brain CT scans: a systematic review.
Traumatic brain injury (TBI) is a leading cause of emergency department visits and a major contributor to injury-related mortality and long-term neurological disability. Non-contrast computed tomography (CT) is the gold-standard imaging modality for the rapid diagnosis of TBI. Clinical outcomes depend strongly on early detection and prompt acute management. Artificial intelligence (AI)-based models may support faster automated identification of traumatic findings and early prediction of patient prognosis. A systematic literature search was conducted in PubMed/MEDLINE, Scopus, IEEE Xplore, ACM Digital Library, and the Cochrane Library in accordance with PRISMA 2020 guidelines to evaluate AI-based models for automated detection of TBI-related findings on CT and for prediction of clinical outcomes. Risk of bias and applicability were assessed using QUADAS-2 for diagnostic accuracy studies and PROBAST + AI for prediction model studies. Twenty-two studies were included. Sixteen studies evaluated diagnostic tasks and 10 evaluated prognostic outcomes, with four studies contributing to both categories. Diagnostic performance was generally high, with many studies reporting AUC values approaching or exceeding 0.90, particularly for larger lesion volumes.Prognostic performance was more variable, with moderate to high discrimination and substantial heterogeneity. Only 9 studies incorporated independent external validation, and performance was frequently lower in external cohorts. All prognostic model studies were judged to be at high overall risk of bias using PROBAST + AI, and most diagnostic accuracy studies also demonstrated high or unclear risk of bias in at least one QUADAS-2 domain, most frequently in patient selection. AI-based models applied to brain CT demonstrate strong technical performance for both diagnostic and prognostic tasks in TBI. However, most studies relied on retrospective designs and lacked independent external validation which limits models generalizability and raises concern for potential overfitting. Prospective, multicenter studies with standardized methodologies and rigorous external validation are required before widespread clinical implementation.
Humans↗
Predicting risk of ischemic stroke: A transformer model using genomic data.
BACKGROUND AND OBJECTIVE: Ischemic stroke is a leading cause of mortality and long-term disability worldwide. Genetic factors contribute to IS susceptibility, yet conventional polygenic risk score approaches are primarily based on additive effects and may not fully capture non-linear relationships or positional context and interactions among genetic variants. This study aimed to develop and evaluate a transformer-based genomic model incorporating position-wise genotype embedding for IS risk prediction. METHODS: We conducted a genome-wide association study using the UK Biobank dataset to identify IS-associated loci. Gene prioritisation was subsequently performed using tissue-specific expression quantitative trait locus-based Mendelian randomisation and colocalization analyses in whole blood and brain cortex. We then developed a transformer-based model that encoded genotype and SNP-position information using a position-wise embedding layer. Model performance was evaluated across three UK Biobank control definitions and externally assessed in the independent All of Us cohort. Performance metrics included the area under the receiver operating characteristic curve (AUROC), precision, recall, and F1 score. RESULTS: Across the three UK Biobank control definitions, the proposed method achieved the numerically highest discrimination among the evaluated models, with AUROCs of 0.8109, 0.7843, and 0.7468 using MRF-negative, combined, and MRF-positive controls, respectively. In the external All of Us cohort, the proposed method achieved an AUROC of 0.7251 and retained the highest AUROC among the evaluated models. In a separate incident-stroke survival analysis, medium- and high-score groups had hazard ratios of 1.13 and 1.21, respectively, relative to the low-score group. A total of 18 IS-associated loci were identified. Among the tissue-specific MR results, EDEM2 in the brain cortex remained significant after Bonferroni correction, while DCHS2 showed a nominal association. CONCLUSIONS: The proposed transformer-based framework provides a genomic modelling approach that achieved the highest discrimination among the evaluated models in this study and retained comparative performance in an independent external cohort. In further applications, integrating this genomic framework with conventional clinical, lifestyle, and environmental risk factors may support more comprehensive and personalised IS risk assessment. Prospective, population-representative, and multi-ancestry validation will be important to establish its potential role in future prevention-oriented risk management.
Genomics and bioinformatics↗
Association between circulating GTP cyclohydrolase 1 concentrations and acute ischemic stroke: an exploratory case-control study in a Chinese population.
BACKGROUND AND OBJECTIVE: Ischemic stroke (IS) is a leading global cause of disability and mortality, characterized by cerebral hypoxia and tissue necrosis. GTP cyclohydrolase 1 (GCH1) regulates endothelial function and oxidative stress; however, whether circulating GCH1 concentrations are altered in acute ischemic stroke (AIS) remains unclear. This exploratory case-control study aimed to investigate plasma GCH1 levels and their associations with clinical characteristics in patients with AIS. METHODS: Seventy-one patients with AIS and 92 controls undergoing routine health examinations were recruited at the Affiliated Hospital of Youjiang Medical University for Nationalities (January 2024-May 2025). Clinical and biochemical data including lipid profiles, C-reactive protein, homocysteine, and National Institutes of Health Stroke Scale (NIHSS) scores (only for patients with AIS) were collected. Plasma GCH1 levels were measured using an enzyme-linked immunosorbent assay. Statistical analyses were performed to evaluate differences between groups and to examine the associations between plasma GCH1 levels and clinical characteristics. Receiver operating characteristic curve analysis was conducted to assess the discriminatory performance of circulating GCH1. RESULTS: Plasma GCH1 concentrations were significantly lower in AIS patients (6.51 ± 3.59 ng/mL vs. 14.32 ± 3.29 ng/mL, p < 0.001). Binary logistic regression analysis showed that lower plasma GCH1 levels were independently associated with AIS (OR = 0.496, 95% CI: 0.385-0.644, p < 0.001), while multiple linear regression analysis demonstrated that AIS was independently associated with lower plasma GCH1 levels (B = -7.687, 95% CI: -9.011 to -6.362, p < 0.001). Plasma GCH1 showed strong discrimination between the two groups (AUC = 0.924, 95% CI: 0.871-0.978) but was not associated with NIHSS scores (Spearman's rho = -0.034, p = 0.778). CONCLUSION: Plasma GCH1 concentrations were lower in patients with AIS than in health-examination controls and showed high apparent discrimination in this dataset. Because GCH1 was measured after stroke onset and the sample-derived threshold was derived in the same case-control sample, these findings do not establish temporality, causality, or clinical diagnostic utility. Prospective multicenter studies including clinically relevant disease controls and independent external validation are required.
Humans↗
Methods for defining equity-stratifying variables: a systematic review of validation studies.
BACKGROUND AND OBJECTIVE: Disease burden is often disproportionally higher among those who are socially disadvantaged by factors defined in the PROGRESS-Plus framework (ie, Place of residence, Race/ethnicity/culture/language, Occupation, Gender/sex, Religion, Education, Socioeconomic status, and Social capital, with "Plus" covering features like age and disability). The accuracy and applicability of case definitions to identify these variables from administrative and clinical health data are unknown. We conducted a systematic review to explore how equity-stratifying variables, as categorized by the PROGRESS-Plus framework, have been defined and validated in epidemiologic studies using administrative health, population-level, or electronic health record (EHR) data. METHODS: Medline, EMBASE, CINAHL, Web of Science, and Google Scholar were searched from the inception of the databases to 2024 for validation studies of equity-stratifying variables in adults using administrative health datasets, health registries, or EHR data. Titles and abstracts, followed by relevant full-text articles, were screened in duplicate by two reviewers for eligibility. The data sources utilized, algorithms employed, and their associated performance measures were extracted and synthesized from included studies. Given substantial heterogeneity in study design, equity-stratifying variable definition, and performance metrics, meta-analysis was not possible. RESULTS: Of the 9099 unique citations screened, 188 full texts were reviewed and 116 were included in this review. Most studies were published between 2019 and 2024 (n = 64, 55%) and were validation studies of race/ethnicity definitions that used race/ethnicity codes or surname list algorithms (n = 66, 57%). No studies examined religion. Regarding the reported performance measure estimates, the race/ethnicity/culture/language equity-stratifying variables category had the largest variability across sensitivity, positive predictive value (PPV), and Cohen's Kappa. Occupation validation studies had the lowest variation in sensitivity and PPV. CONCLUSION: Despite an increasing number of publications reporting on the validation of equity-stratifying variables relevant to the PROGRESS-Plus framework, performance measures varied widely across studies. The significant heterogeneity in equity-stratifying variable definitions and methods used to validate them support the need for further rigorous validation of equity-stratifying variables in administrative and clinical health data. PLAIN LANGUAGE SUMMARY: Disease burden is often higher in people who experience financial hardships, lower level of education, discrimination due to race/ethnicity, and unstable housing. These social factors can be considered health equity factors and are important for understanding health inequalities. Health researchers often use large datasets, such as hospital or electronic health records (EHRs), to study these health equity factors. However, it is not clear how accurately these data sources capture information about people's social circumstances and how these factors are defined. In this study, we reviewed existing research to understand how health equity factors have been defined across health data sources and how accurate they are at measuring aspects of health equity and social disadvantage. Of the more than 9000 studies we identified, we included 116 that met our criteria for this systematic review. Most included studies focused on identifying race and ethnicity, often using codes or surname-based methods. We found that the accuracy of these methods varied widely across studies, meaning results may not always be reliable or comparable. Overall, our findings show that there are inconsistencies in how social factors are defined and measured in health data. This makes it difficult to fully understand and address health inequalities using routinely collected health data. More work is needed to develop and validate better quality and more consistent methods for capturing these important social factors.
Humans↗