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1.
J Psychopharmacol ; : 2698811241269751, 2024 Aug 13.
Artículo en Inglés | MEDLINE | ID: mdl-39135496

RESUMEN

BACKGROUND: Psychedelic drugs have been of interest in medicine since the early 1950s. There has recently been a resurgence of interest in psychedelics. AIMS: The objective of this study is to determine the extent of the available literature on psilocybin for medical indications including the designs used, study characteristics, indications studied, doses, and authors' conclusions. We identify areas for further study where there are research gaps. METHODS: We conducted a systematic scoping review of clinical indications for psilocybin, encompassing psychiatric and medical conditions. We systematically searched Medline and Embase using keywords related to psilocybin. We reviewed titles and texts in duplicate using Covidence software. We extracted data individually in duplicate using Covidence software and a senior reviewer resolved all author conflicts. We analyzed data descriptively. RESULTS: We included 193 published and 80 ongoing studies. Thirty-seven percent of included studies were systematic reviews. Only 12% of included studies were randomized controlled trials. The median number of participants was 22 with a median of 18 participants who had taken psilocybin. Thirty-eight percent of studies reported at least one potential conflict of interest. The most common indication was depression (28%). Also commonly studied were substance use (14%), mental health in life-threatening illness (9%), headaches (6%), depression and anxiety (6%), obsessive-compulsive disorder (3%), and anxiety disorders (3%). CONCLUSIONS: Most studies involving the administration of psilocybin have small sample sizes and the most common focus has been psychiatric disorders. There is a need for high-quality randomized trials on psilocybin and to expand consideration to other promising indications, such as chronic pain.

2.
Pilot Feasibility Stud ; 10(1): 30, 2024 Feb 15.
Artículo en Inglés | MEDLINE | ID: mdl-38360686

RESUMEN

BACKGROUND: Knee arthritis is a leading cause of limited function and long-term disability in older adults. Despite a technically successful total knee arthroplasty (TKA), around 20% of patients continue to have persisting pain with reduced function, and low quality of life. Many of them continue using opioids for pain control, which puts them at risk for potential long-term adverse effects such as dependence, overdose and risk of falls. Although persisting pain and opioid use after TKA have been recognised to be important issues, individual strategies to decrease their burden have limitations and multi-component interventions, despite their potential, have not been well studied. In this study, we propose a multi-component pathway including personalized pain management, facilitated by a pain management coordinator. The objectives of this pilot trial are to evaluate feasibility (recruitment, retention, and adherence), along with opioid-free pain control at 8 weeks after TKA. METHODS: This is a protocol for a multicentre pilot randomised controlled trial using a 2-arm parallel group design. Adult participants undergoing unilateral total knee arthroplasty will be considered for inclusion and randomised to control and intervention groups. Participants in the intervention group will receive support from a pain management coordinator who will facilitate a multicomponent pain management pathway including (1) preoperative education on pain and opioid use, (2) preoperative risk identification and mitigation, (3) personalized post-discharge analgesic prescriptions and (4) continued support for pain control and recovery up to 8 weeks post-op. Participants in the control group will undergo usual care. The primary outcomes of this pilot trial are to assess the feasibility of participant recruitment, retention, and adherence to the interventions, and key secondary outcomes are persisting pain and opioid use. DISCUSSION: The results of this trial will determine the feasibility of conducting a definitive trial for the implementation of a multicomponent pain pathway to improve pain control and reduce harms using a coordinated approach, while keeping an emphasis on patient centred care and shared decision making. TRIAL REGISTRATION: Prospectively registered in Clinicaltrials.gov (NCT04968132).

3.
Contemp Clin Trials Commun ; 33: 101132, 2023 Jun.
Artículo en Inglés | MEDLINE | ID: mdl-37122488

RESUMEN

Background: Bayesian methods are being used more frequently in orthopaedics. To advance the use and transparent reporting of Bayesian studies, reporting guidelines have been recommended. There is currently little known about the use or applications of Bayesian analysis in orthopedics including adherence to recommended reporting guidelines. The objective is to investigate the reporting of Bayesian analysis in orthopedic surgery studies; specifically, to evaluate if these papers adhere to reporting guidelines. Methods: We searched PUBMED to December 2nd, 2020. Two reviewers independently identified studies and full-text screening. We included studies that focused on one or more orthopaedic surgical interventions and used Bayesian methods. Results: After full-text review, 100 articles were included. The most frequent study designs were meta-analysis or network meta-analysis (56%, 95% CI 46-65) and cohort studies (25%, 95% CI 18-34). Joint replacement was the most common subspecialty (33%, 95% CI 25-43). We found that studies infrequently reported key concepts in Bayesian analysis including, specifying the prior distribution (37-39%), justifying the prior distribution (18%), the sensitivity to different priors (7-8%), and the statistical model used (22%). In contrast, general methodological items on the checklists were largely well reported. Conclusions: There is an opportunity to improve reporting quality and transparency of orthopaedic studies using Bayesian analysis by encouraging adherence to reporting guidelines such as ROBUST, JASP, and BayesWatch. There is an opportunity to better report prior distributions, sensitivity analyses, and the statistical models used.

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