Purpose: To investigate patient and clinician perspectives on what is considered important to include in a decision aid for replacement of a missing tooth with an implant.
Methods: An online modified Delphi method with pair comparisons technique was used to survey participants (66 patients, 48 prosthodontists, 46 periodontists, and 31 oral surgeons) in Ontario, Canada from November 2020 to April 2021 regarding the importance of information provided during an implant consultation. Round one included 19 items derived from the literature and informed consent protocols. The decision to retain an item was based on group consensus, defined as at least 75% of participants identifying the item as "important" or "highly important." After analysis of round one results, a second-round survey was sent to all participants to rank the relative importance of the consensus items. Statistical testing was completed using the Kruskal-Wallis one-way analysis of variance test and post hoc Mann-Whitney U tests with a significance level set at p ≤ 0.05.
Results: The first and second surveys had response rates of 77.0% and 45.6%, respectively. In round one, all items except purpose of steps reached group consensus. In round two, the highest group ranked items were patient responsibilities for treatment success and follow-ups after treatment. The lowest group ranked items were cost factors and restorative steps. Significant differences between the stakeholder groups were found on several items, including diagnosis (p ≤ 0.00), non-implant options (p ≤ 0.00), and cost (p ≤ 0.01). In general, patients' opinions were significantly different than clinicians' opinions on the relative importance of items.
Conclusions: Clinicians and patients feel that multiple items are important to include in a decision aid for implant therapy; however, differences exist between patients and clinicians on the relative importance of items.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1111/jopr.13691 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
The Significance of a Cerebrovascular Accident Outcome Prediction Model for Patients, Family Members, and Health Care Professionals: Qualitative Evaluation Study.
JMIR Hum Factors
January 2025
Department of Value Improvement, St. Antonius Hospital, Nieuwegein, Netherlands.
Background: Patients with cerebrovascular accident (CVA) should be involved in setting their rehabilitation goals. A personalized prediction of CVA outcomes would allow care professionals to better inform patients and informal caregivers. Several accurate prediction models have been created, but acceptance and proper implementation of the models are prerequisites for model adoption.
View Article and Find Full Text PDFHome-Based Intervention Tool for Cardiac Telerehabilitation: Protocol for a Controlled Trial.
JMIR Res Protoc
January 2025
Clinical Physiology Institute, Consiglio Nazionale delle Ricerche, Pisa, Italy.
Background: Among cardiovascular diseases, adult patients with congenital heart disease represent a population that has been continuously increasing, which is mainly due to improvement of the pathophysiological framing, including the development of surgical and reanimation techniques. However, approximately 20% of these patients will require surgery in adulthood and 40% of these cases will necessitate reintervention for residual defects or sequelae of childhood surgery. In this field, cardiac rehabilitation (CR) in the postsurgical phase has an important impact on the patient by improving psychophysical and clinical recovery in reducing fatigue and dyspnea to ultimately increase survival.
View Article and Find Full Text PDFA bird's-eye view of the biological mechanism and machine learning prediction approaches for cell-penetrating peptides.
Front Artif Intell
January 2025
Department of Genetic Engineering, Computational Biology Lab, School of Bioengineering, SRM Institute of Science and Technology, SRM Nagar, Chennai, India.
Cell-penetrating peptides (CPPs) are highly effective at passing through eukaryotic membranes with various cargo molecules, like drugs, proteins, nucleic acids, and nanoparticles, without causing significant harm. Creating drug delivery systems with CPP is associated with cancer, genetic disorders, and diabetes due to their unique chemical properties. Wet lab experiments in drug discovery methodologies are time-consuming and expensive.
View Article and Find Full Text PDFA Novel Model Using ML Techniques for Clinical Trial Design and Expedited Patient Onboarding Process.
Clinicoecon Outcomes Res
January 2025
Public Systems Group, Indian Institute of Management Ahmedabad, Ahmedabad, Gujarat, India.
Introduction: Clinical trials are critical for drug development and patient care; however, they often need more efficient trial design and patient enrolment processes. This research explores integrating machine learning (ML) techniques to address these challenges. Specifically, the study investigates ML models for two critical aspects: (1) streamlining clinical trial design parameters (like the site of drug action, type of Interventional/Observational model, etc) and (2) optimizing patient/volunteer enrolment for trials through efficient classification techniques.
View Article and Find Full Text PDFDevelopment and validation of a clinical features-based nomogram for predicting neonatal cerebral microbleeds.
Quant Imaging Med Surg
January 2025
Department of Radiology, The First Hospital of Tsinghua University, Beijing, China.
Background: Neonatal cerebral microbleeds (CMBs) occur infrequently, and during the initial phase, they often present without noticeable clinical symptoms, which can result in delays in both diagnosis and treatment. There has been relatively little research conducted on neonatal CMBs, with even less focus on their related risk factors. However, identifying risk factors and proactively preventing microbleeds is particularly crucial for effective treatment.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!