Background: Although there is broad agreement that the accurate estimation of non-adherence rates in clinical trials is essential to determining the dose-response relationship, treatment safety and efficacy effects, no accurate estimates have ever been produced.
Methods: This study used a novel platform combining artificial intelligence and virtual patient monitoring to identify and quantify the scope of unreported intentional non-adherence in clinical trials of new medical therapies. Nearly 260,000 observations were drawn from a convenience sample of 2976 study volunteers participating in 23 clinical trials of psychiatric, neurological and neuromuscular diseases.
Results: The results indicate that 4% of all confirmed doses were intentionally non-adherent, 48% of all study volunteers had at least one intentionally non-adherent dose and 5% of study volunteers were intentionally non-adherent for more than one-third of all doses required.
Conclusions: Several factors were associated with, and predictive of, unreported intentional non-adherence including clinical trial phase; clinical trial duration; geographic location where the study was conducted; and investigative site enrollment volume. The findings also show that although the overall rate of intentional non-adherence does not change over the course of a clinical trial, study volunteers who deliberately chose not to take their first dose had a mean intentional non-adherence rate five times higher than that observed among those who were first dose adherent. Implications of the study results are discussed.
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http://dx.doi.org/10.1007/s43441-020-00155-x | DOI Listing |
Trials
January 2025
Department of Anesthesiology and Pain Medicine, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-Ro 43-Gil, Songpa-Gu, Seoul, 05505, Korea.
Background: Prophylactic parenteral administration of antibiotics is strongly recommended to prevent surgical site infection (SSI). Cefoxitin is mainly administered intravenously in colorectal surgery. The current standard method for administering prophylactic antibiotics in adults is to administer a fixed dose quickly before skin incision.
View Article and Find Full Text PDFJ Biomed Sci
January 2025
Tumour Targeting Laboratory, Olivia Newton-John Cancer Research Institute, Melbourne, VIC, 3084, Australia.
Research into cancer treatment has been mainly focused on developing therapies to directly target cancer cells. Over the past decade, extensive studies have revealed critical roles of the tumour microenvironment (TME) in cancer initiation, progression, and drug resistance. Notably, cancer-associated fibroblasts (CAFs) have emerged as one of the primary contributors in shaping TME, creating a favourable environment for cancer development.
View Article and Find Full Text PDFBMC Public Health
January 2025
Department of Clinic Nutrition, Nanjing Drum Tower Hospital, Affiliated Hospital of Nanjing University Medical School, Nanjing, Jiangsu, China.
Background: Since diet is a known modulator of inflammation, the Dietary Inflammatory Index (DII), which quantifies the inflammatory potential of an individual's diet, becomes a significant parameter to consider. Chronic diarrhea is commonly linked to inflammatory processes within the gut. Thus, this study aimed to explore the potential link between DII and chronic diarrhea.
View Article and Find Full Text PDFBMC Neurol
January 2025
General Physician, Arab Care Hospital, Ramallah, 00970, Palestine.
Background: Trigeminal neuralgia (TN) is a prevalent and debilitating craniofacial pain disorder characterized by severe, unilateral, shock-like pain. Standard treatments include anti-epileptic drugs and surgical interventions, but many patients experience limited relief or adverse effects. Non-invasive therapies, such as transcutaneous electrical nerve stimulation (TENS), have emerged as alternative options.
View Article and Find Full Text PDFBMC Bioinformatics
January 2025
Centro de Salud Retiro, Hospital Universitario Gregorio Marañon, C/Lope de Rueda, 43, 28009, Madrid, Spain.
Background: Natural language processing (NLP) enables the extraction of information embedded within unstructured texts, such as clinical case reports and trial eligibility criteria. By identifying relevant medical concepts, NLP facilitates the generation of structured and actionable data, supporting complex tasks like cohort identification and the analysis of clinical records. To accomplish those tasks, we introduce a deep learning-based and lexicon-based named entity recognition (NER) tool for texts in Spanish.
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