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Unlabelled: To test the effect of a new decision support tool for acute appendicitis and assess its efficacy and acceptability.
Background: Mounting evidence from randomized controlled trials have shown that antibiotics can be a safe and effective treatment for appendicitis. Patients and surgeons must work together to choose the optimal treatment approach for each patient based on their own preferences and values. We developed a decision support tool to facilitate shared decision-making for appendicitis and its effect on decisional outcomes remains unknown.
Methods: We conducted an online randomized field test in at-risk individuals comparing the decision support tool to a standard infographic. Individuals were randomized 3:1 to view the decision support tool or infographic. The primary outcome was the total decisional conflict scale (DCS) score measured before and after exposure to the decision support tool. Secondary outcomes included between-group DCS scores, and between-group comparisons of the acceptability.
Results: One hundred eighty individuals were included in the study. Total DCS scores decreased significantly after viewing the decision support tool (59 [95% confidence interval (CI): 55-63] to 15 [95% CI: 12-17], < 0.001) representing movement from a state of high to low decisional conflict. Individuals exposed to the decision support tool reported higher acceptability ratings (3.7 [95% CI: 3.6-3.8] vs 3.3 [95% CI: 3.2-3.5] out of 4) and demonstrated increased willingness to consider both treatment options.
Conclusions: These data support the further use and testing of this novel decision support tool in patients with acute appendicitis.
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http://dx.doi.org/10.1097/AS9.0000000000000213 | DOI Listing |
Knee Surg Relat Res
September 2025
Florida Orthopaedic Institute, Gainesville, FL, 32607, USA.
Background: A clear understanding of minimal clinically important difference (MCID) and substantial clinical benefit (SCB) is essential for effectively implementing patient-reported outcome measurements (PROMs) as a performance measure for total knee arthroplasty (TKA). Since not achieving MCID and SCB may reflect suboptimal surgical benefit, the primary aim of this study was to use machine learning to predict patients who may not achieve the threshold-based outcomes (i.e.
View Article and Find Full Text PDFBMC Med Inform Decis Mak
September 2025
Emergency Department, Helios Spital, Überlingen, Germany.
Background: The increasing amount of data routinely collected on ICUs poses a challenge for clinicians which is aggravated with data-heavy therapies like Continuous Kidney Replacement Therapy (CKRT). We developed the CKRT Supporting Software Prototype (CKRT-SSP), a clinical decision support system for use before, during and after CKRT. The aim of this user experience (UX) study was to prospectively evaluate CKRT-SSP in terms of usability, user experience, and workload in a simulated ICU setting.
View Article and Find Full Text PDFJ Assist Reprod Genet
September 2025
Department of Gynecology, Pingxiang Maternal and Child Health Hospital, PingXiang, Jiangxi, China.
Objective: This study aimed to identify key predictors of uterine fibroid (UF) recurrence following laparoscopic myomectomy (LM) in reproductive-age women and to construct a predictive nomogram to support individualized clinical decision-making.
Methods: This retrospective cohort study included 459 women who underwent LM. Recurrence of UFs and risk of recurrence were analyzed.
Geroscience
September 2025
Department of Emergency and Internal Medicine, Skåne University Hospital, Malmö, Sweden.
To evaluate a simplified version of the Clinical Frailty Scale (SCFS) among older adults presenting to the emergency department (ED) with acute dyspnea. In this retrospective single-center cohort study, we included patients from the Acute Dyspnea Study (ADYS) cohort. Severity of illness was assessed using the Medical Emergency Triage and Treatment System (METTS).
View Article and Find Full Text PDFJ Anesth
September 2025
Community Medicine Education Promotion Office, Faculty of Medicine, Kagawa University Ikenobe, 1750-1, Miki-Cho, Kagawa, 761-0793, Japan.
Generative artificial intelligence (AI) is rapidly transforming perioperative medicine, particularly anesthesiology, by enabling novel applications, such as real-time data synthesis, individualized risk prediction, and automated documentation. These capabilities enhance clinical decision-making, patient communication, and workflow efficiency in the operating room. In education, generative AI offers immersive simulations and tailored learning experiences that improve both technical skills and professional judgment.
View Article and Find Full Text PDF