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To Pause With a Cliffhanger or a Temporary Closure? The Differential Impact of Serial Versus Episodic Narratives on Children\u27s Physical Activity Behaviors
No full textResearch has supported the effectiveness of narratives for promoting health behavior, but different narrative presentation formats (serial vs. episodic) have seldom been compared. Suspense theories suggest that serial narratives, which do not provide a full resolution at the end of an episode, may create higher motivation for continued engagement with a story. Forty-four 8 to 12-year-old children were randomly assigned to watch an animation series designed for an existing active video game in which the plot was delivered either continuously across multiple episodes (serial) or in multiple yet relatively independent self-contained episodes (episodic). Controlling for social desirability, children who watched the serial narrative had significantly more moderate to vigorous physical activity (MVPA) and step counts while the episodic group\u27s gameplay duration decreased, especially during later visits. There was no difference in self-reported narrative immersion or physical activity intention. Serial narratives can result in more time spent in MVPA behaviors than episodic narratives
Therapeutic Hurdles in Acute Myeloid Leukemia: Leukemic Stem Cells, Inflammation and Immune Dysfunction
No full textAcute myeloid leukemia (AML) is an aggressive and highly heterogeneous hematological malignancy characterized by clonal expansion and differentiation arrest in myeloid progenitor cells. Despite advancements in chemotherapy, allogeneic hematopoietic stem cell transplantation, and post-remission maintenance therapies, the long-term survival remains unsatisfactory with high rates of relapse and refractory. These therapeutic challenges are mediated by multiple factors, including the complexity of the cellular hierarchies in AML, the interaction of leukemic stem cells (LSCs) with the bone marrow niche, inflammation, and immune evasion mechanisms. Further, the absence of specific surface markers that distinguish LSCs from normal hematopoietic stem cells, together with LSCs\u27 functional heterogeneity, complicates targeted treatment approaches. Immune dysfunction, including T cell exhaustion and immune suppression within the bone marrow niche contributes to therapy resistance. In this brief review, we aim to explore current challenges in AML therapy, focusing on LSC-driven resistance, immune evasion, and the need for innovative therapeutic strategies
Quantitative Ablation Confirmation Methods in Percutaneous Thermal Ablation of Malignant Liver Tumors: Technical Insights, Clinical Evidence, and Future Outlook
No full textPercutaneous image-guided thermal ablation is an established local curative-intent treatment technique for the treatment of primary and secondary malignant liver tumors. Whereas margin assessment after surgical resection can be accomplished with microscopic examination of the resected specimen, margin assessment after percutaneous thermal ablation relies on cross-sectional imaging. The critical measure of technical success is the minimal ablative margin (MAM), defined as the minimum distance between the tumor and the edge of the ablation zone. Traditionally, the MAM has been assessed qualitatively using anatomic landmarks, which has suboptimal accuracy and reproducibility and is prone to operator bias. Consequently, specialized software-based methods have been developed to standardize and automate MAM quantification. In this review, the authors discuss the technical components of such methods, including image acquisition, segmentation, registration, and MAM computation, define the sources of measurement error, describe available software solutions in terms of image processing techniques and modes of integration, and outline the current clinical evidence, which strongly supports the use of such dedicated software. Finally, the authors discuss current logistical and financial barriers to widespread use of ablation confirmation methods as well as potential solutions
Externally Validated Digital Decision Support Tool for Time-to-Osteoradionecrosis Risk-Stratification Using Right-Censored Multi-Institutional Observational Cohorts
No full textBackground: Existing studies on osteoradionecrosis of the jaw (ORNJ) have primarily used cross-sectional data, assessing risk factors at a single time point. Determining the time-to-event profile of ORNJ has important implications to monitor oral health in head and neck cancer (HNC) long-term survivors.
Methods: Data were retrospectively obtained for a clinical observational cohort of 1129 patients (198 ORNJ cases) with HNC treated with radiotherapy (RT) at The University of Texas MD Anderson Cancer Center. A Weibull Accelerated Failure Time model was trained on previously identified dosimetric, clinical and demographic predictors. External validation was performed using an independent cohort of 265 patients (92 ORNJ cases) treated at Guy\u27s and St. Thomas\u27 Hospitals. To facilitate clinical implementation of the model, an online graphical user interface (GUI) was developed, including formal stakeholder usability testing.
Results: Our model identified that gender (males), pre-RT dental extractions and D25% were associated with a 38 %, 27 % and 12 % faster onset of ORNJ, respectively, with adjusted time ratios of 0.62 (p = 0.11), 0.73 (p = 0.13) and 0.88 (p \u3c 0.005). The model demonstrated strong internal calibration (integrated Brier score of 0.133, D-calibration p-value 0.998) and optimal discrimination at 72 months (Harrell\u27s C-index of 0.72).
Conclusion: This study is the first to demonstrate a direct relationship between radiation dose and the time to ORNJ onset, providing a novel characterization of the impact of delivered dose and patient-related factors not only on the probability of a late effect (ORNJ), but the conditional risk during survivorship
Improving the Oral Pathology Referral Process in an Academic Dental Institution
No full textIntroduction: Inefficiencies in the oral cancer referral process pose serious risks, including delayed diagnoses and poor patient outcomes. This project aimed to improve the reliability and efficiency of referrals between dental students and oral pathology residents for patients with suspected oral cancer. Although early detection is widely acknowledged as critical, significant process breakdowns were identified, including the absence of a standardized workflow, inconsistent follow-up practices, and a lack of accountability and tracking mechanisms. As a result, approximately half of the patients recommended for biopsy did not undergo the procedure, leading to delayed diagnoses and potentially worse prognoses.
Methodology: To address these challenges, a systems-based redesign approach was employed using systems engineering and human factors design principles. Tools such as Failure Modes and Effects Analysis and the Systems Engineering Initiative for Patient Safety framework were used to identify systemic vulnerabilities and analyze interdependencies among people, tasks, tools, and the organizational environment. These analyses revealed that the referral process was excessively dependent on individual diligence and vulnerable during care transitions. A comprehensive intervention toolkit was developed, including standardized referral forms, clearly defined roles and responsibilities, new workflow protocols, a dedicated care coordination role, and plans for integration into the electronic health record (EHR) platform. The implementation followed an iterative model, incorporating continuous feedback from end users and performance metrics to guide refinement.
Results: Initial results demonstrated a marked reduction in referral-to-biopsy turnaround times, from a median of 56 days in 2022 to just 4 days in 2024, indicating substantial gains in process efficiency. However, overall referral loop closure rates initially remained below target due to continued reliance on manual tracking systems. Although integration into the EHR platform was approved, it had not yet been implemented during the study period. In particular, the most significant improvements occurred in the final quarter of 2024, with loop closure rates reaching 83%, coinciding with the introduction of a dedicated care coordinator role. This finding underscores the critical role of dedicated personnel in mitigating the limitations of lower-reliability interventions, such as ad-hoc communication and manual follow-up.
The insights of the later phase further emphasized the importance of digital support systems, prompting plans to automate referral tracking and escalation alerts within the EHR to reduce variability and improve consistency. Furthermore, the data highlighted the potential of a risk stratified referral management approach, enabling prioritization of high-risk cases (e.g., suspected malignancies) for more intensive monitoring.
Conclusion: This project demonstrated how systems engineering and human factors design can be leveraged to transform referral reliability in complex clinical settings. The outcomes provide a scalable model for improving care transitions and early cancer detection across broader healthcare environments
The Impact of Post-Remission Granulocyte Colony-Stimulating Factor Use in the Phase 3 Studies of Venetoclax Combination Treatments in Patients With Newly Diagnosed Acute Myeloid Leukemia
No full textStandardizing Social Determinants of Health Factors from Heterogeneous Sources to Improve Data FAIRness
No full textSocial Determinants of Health (SDoH) significantly influence health outcomes, yet their representation in computational models remains fragmented. This dissertation addresses this gap by constructing an SDoH ontology (SDoHO), leveraging it to improve large language model (LLM) performance, and using LLMs to extract new knowledge and enrich the ontology. The overarching goal is to establish a feedback loop where ontology development enhances LLM-based extraction, and LLM-derived insights refine and expand the ontology. The methodology is structured across three aims: (1) ontology construction, (2) ontology-assisted LLM enhancement, and (3) LLM-driven ontology enrichment, with a focus on Alzheimer’s Disease and Related Dementias (ADRD).
Aim 1 involves the development of SDOHO, a structured knowledge representation of key SDoH factors. Represented in OWL2, SDoHO is a comprehensive ontology comprising 708 classes, 106 object properties, and 20 data properties, with 1,561 logical axioms and 976 declaration axioms. The ontology features a well-defined class hierarchy spanning six levels, with a primary top-level structure composed of nine categories. This structured representation provides a robust foundation for standardizing and organizing SDOH knowledge, facilitating improved automated knowledge extraction and integration with downstream applications.
Aim 2 examines the effectiveness of using ontology hierarchies to enhance LLM-based SDOH information extraction. This approach focuses on extracting 17 first-level SDoH labels and 45 second-level labels from MIMIC-III text data. Experimental results on 153 annotated files reveal that GPT-4 generally outperforms or is more stable than LLaMA 3, with F1 scores ranging from 0.48 to 0.64 across different methods. Notably, the hybrid method for GPT-4 attained an F1 score of 0.6364, while LLaMA 3 achieved the highest F1 score of 0.64. A false positive error management strategy led to maximum precision improvements of ~9% and F1 score gains of ~6%, highlighting its effectiveness in addressing critical limitations of LLMs. These findings underscore the benefits and challenges of integrating structured knowledge with generative AI models for domain-specific concept extraction.
Aim 3 extends SDoHO’s application to ADRD by using LLMs to extract SDoH-ADRD concepts and infer relationships from annotated literature. SDoH and ADRD concept extraction achieved F1 scores of 0.744 and 0.927, respectively, while SDoH concept pairing with relationship identification reached an F1 score of 0.914. Instance mapping achieved an F1 score of 0.69, identifying five unmatched instances that suggest potential new concepts for existing SDoH ontologies. Additionally, a focused analysis on suicidal ideation and SDoH-related factors within ADRD populations revealed overlooked social stressors, demonstrating the ontology’s potential for broader health informatics applications. Additionally, a focused analysis on suicidal ideation and SDoH-related factors reveals overlooked social stressors, demonstrating the ontology’s potential for broader health informatics applications.
Overall, this dissertation demonstrates a possible bidirectional integration of ontology and LLMs, where ontologies structure and refine LLM outputs, and LLM-extracted knowledge feeds back to enhance the ontology. By bridging structured knowledge representation with generative AI, this work advances ontology-driven natural language processing, improving automated concept extraction, relationship inference, and domain-specific knowledge enrichment. Future directions include expanding evaluation datasets, refining ontology-informed prompting techniques, and assessing real-world applications in clinical decision support and public health policy
Journal of Family of Strengths Website Re-Designed!
Get PDFThe Journal of Family Strengths (JFS), formerly Family Preservation Journal, is an open-access, double-blind peer-reviewed online journal produced by CHILDREN AT RISK in partnership with the Texas Juvenile Crime Prevention Center – Prairie View A&M University and The TMC Library.
For additional information, visit the Library Blo
Recognizing Problematic Journals
No full textCabells and Third Iron have collaborated to bring Cabells’ awareness of problematic journals and websites to LibKey! Using Cabells’ data, LibKey will now highlight when an article or website domain is associated with a journal or URL labeled as predatory in Cabells’ Journalytics data