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Objectives: Borderline intelligence has been excluded from formal diagnostic systems and not included in disability diagnoses, leaving gaps in treatment, education, and social welfare despite various functional impairments. Therefore, we aimed to shed light on ways to enhance the intelligence and functioning of individuals with borderline intelligence by reviewing research on its progression, education, and treatment.
Methods: Ten studies that met the inclusion and exclusion criteria were included in the final literature review and analyzed according to detailed topics (participant characteristics, design, and results).
Results: Borderline intelligence is associated with various comorbid conditions, such as anxiety, depression, attention deficit/hyperactivity disorder, and addictive disorders, which negatively impact its course and prognosis. Individuals with borderline intelligence often face challenges in academics, employment, interpersonal relationships, and health owing to lifelong cognitive impairments. The treatment of borderline intelligence necessitates addressing environmental factors, such as neglect and abuse, as well as treating comorbid mental disorders, which are crucial for prognosis. Tailoring treatment programs for cognitive profile characteristics have been proposed, and studies have reported the effectiveness of pharmacotherapy, working memory training, and intensive rehabilitation training. Therefore, early intervention during childhood brain development is necessary. Risk factors, such as lack of parental education, and their impact on treatment outcomes have also been reported.
Conclusion: Extensive research is needed on education, treatment, and prognosis related to borderline intelligence. Active intervention for children with borderline intelligence is essential to improve their functioning and quality of life.
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http://dx.doi.org/10.5765/jkacap.240014 | DOI Listing |
Front Oncol
August 2025
MedSol AI Solutions, University of Pretoria, Pretoria, South Africa.
Background: This study evaluates the performance of a deep learning-based artificial intelligence (AI) system developed under the Stradexa (a branded form of doxorubicin used regionally in South Africa) initiative, designed for real-time risk stratification and treatment monitoring in HER2-positive breast cancer. Conducted in a routine clinical setting, the system's predictive capacity was assessed by comparing AI-generated risk scores derived from B-mode ultrasound with histopathology, immunohistochemistry, and treatment response in patients undergoing trastuzumab or doxorubicin therapy. The AI tool demonstrated favorable diagnostic accuracy and a meaningful correlation between risk score reduction and tumor response during therapy, particularly in the trastuzumab group.
View Article and Find Full Text PDFBMC Med Inform Decis Mak
September 2025
PRACTIS Clinician Scientist Program, Dean's Office for Academic Career Development, Hannover Medical School, Hannover, Germany.
Background: To synthesize the results of various studies on the readability of ChatGPT and Bard in medical communication.
Methods: Systemic literature research was conducted in PubMed, Ovid/Medline, CINAHL, Web-of-Science, Scopus and GoogleScholar to detect relevant publications (inclusion criteria: original research articles, English language, medical topic, ChatGPT-3.5/-4.
Hum Reprod
September 2025
IVIRMA Global Research Alliance, IVIRMA Madrid, Madrid, Spain.
Study Question: Can a prediction model classify IVF patients into distinct prognostic groups based on their expected yield of euploid blastocysts?
Summary Answer: Five distinct prognostic groups were identified, with chance of obtaining at least one euploid blastocyst ranging from <1% to 2% in very poor to ∼95% in very good prognosis groups.
What Is Known Already: Euploid blastocyst yield is a critical determinant of IVF success. While female age strongly influences embryo euploidy, other factors like ovarian reserve markers, partner age, and BMI may also contribute.
J Am Soc Echocardiogr
August 2025
Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway; Clinic of Cardiology, St. Olavs University Hospital, Trondheim, Norway.
Background: Left ventricular (LV) global longitudinal strain (GLS) offers advantages over LV ejection fraction, including improved diagnostic sensitivity, reproducibility, and prognostic value. However, current semi-automatic analyses are time-consuming and operator-dependent, impeding widespread adoption of GLS in routine clinical practice.
Objectives: We aimed to assess the feasibility, precision, and time-efficiency of GLS measurements using a deep learning (DL) platform that performs real-time GLS analysis during image acquisition and incorporates DL tools to support standardization, to evaluate whether DL-assisted acquisitions can enhance image quality metrics relevant to strain analyses.
Int J Psychol
October 2025
University of New England, Armidale, Australia.
This meta-analysis synthesised findings on the association between emotional intelligence (EI) and borderline personality disorder (BPD) and evaluated potential moderators. Studies were sourced through systematic searches of four databases in April 2024. Studies had to report effect size data and participant numbers to be included in the meta-analysis.
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