Effects of whole-body vibration on chronic ankle instability: a systematic review.

Background and objective: Chronic ankle instability (CAI) is one of the most common sports injuries, and whole-body vibration (WBV) training has been used lately as a potential rehabilitation modality for these patients. The authors conducted a systematic review and meta-analysis to assess whether WBV training positively affects patients with CAI. Materials and methods: The authors systematically searched four databases, including MEDLINE (PubMed), Scopus, Web of Science, and Cochrane Central Register of Controlled Trials, for randomized and non-randomized trials evaluating the effects of WBV on individuals with CAI. The authors used Cochrane RoB2 to assess the risk of bias in randomized trials. A meta-analysis was conducted if three or more studies measured the same outcome. Effect estimates were pooled using a random-effects model. Results: Results were retrieved from seven articles encompassing 288 participants who had CAI. The reach distance of the Star Excursion Balance Test (SEBT) was regarded as the study's main finding. The authors saw a significant training effect on certain planes of motion on dynamic balance. The findings showed that the post-intervention measurements in the WBV group compared to control groups showed improvements in the posterolateral, posteromedial, and medial directions, respectively. There were also promising results on improvements in muscle activity, strength, and proprioception sense measurements with a great diversity in the reported parameters. Conclusion: The authors observed a significant WBV training effect on dynamic balance over posterolateral, posteromedial, and medial reach distances. These findings suggest future studies on the effects of WBV on muscle activity, strength, and proprioception in addition to dynamic and static balance.

Automated diagnosis of autism with artificial intelligence: State of the art.

Autism spectrum disorder (ASD) represents a panel of conditions that begin during the developmental period and result in impairments of personal, social, academic, or occupational functioning. Early diagnosis is directly related to a better prognosis. Unfortunately, the diagnosis of ASD requires a long and exhausting subjective process. We aimed to review the state of the art for automated autism diagnosis and recognition in this research. In February 2022, we searched multiple databases and sources of gray literature for eligible studies. We used an adapted version of the QUADAS-2 tool to assess the risk of bias in the studies. A brief report of the methods and results of each study is presented. Data were synthesized for each modality separately using the Split Component Synthesis (SCS) method. We assessed heterogeneity using the I 2 statistics and evaluated publication bias using trim and fill tests combined with ln DOR. Confidence in cumulative evidence was assessed using the GRADE approach for diagnostic studies. We included 344 studies from 186,020 participants (51,129 are estimated to be unique) for nine different modalities in this review, from which 232 reported sufficient data for meta-analysis. The area under the curve was in the range of 0.71-0.90 for all the modalities. The studies on EEG data provided the best accuracy, with the area under the curve ranging between 0.85 and 0.93. We found that the literature is rife with bias and methodological/reporting flaws. Recommendations are provided for future research to provide better studies and fill in the current knowledge gaps.

Abstract screening using the automated tool Rayyan: results of effectiveness in three diagnostic test accuracy systematic reviews.

OBJECTIVE: To evaluate the performance of the automated abstract screening tool Rayyan. METHODS: The records obtained from the search for three systematic reviews were manually screened in four stages. At the end of each stage, Rayyan was used to predict the eligibility score for the remaining records. At two different thresholds (≤2.5 and < 2.5 for exclusion of a record) Rayyan-generated ratings were compared with the decisions made by human reviewers in the manual screening process and the tool's accuracy metrics were calculated. RESULTS: Two thousand fifty-four records were screened manually, of which 379 were judged to be eligible for full-text assessment, and 112 were eventually included in the final review. For finding records eligible for full-text assessment, at the threshold of < 2.5 for exclusion, Rayyan managed to achieve sensitivity values of 97-99% with specificity values of 19-58%, while at the threshold of ≤2.5 for exclusion it had a specificity of 100% with sensitivity values of 1-29%. For the task of finding eligible reports for inclusion in the final review, almost similar results were obtained. DISCUSSION: At the threshold of < 2.5 for exclusion, Rayyan managed to be a reliable tool for excluding ineligible records, but it was not much reliable for finding eligible records. We emphasize that this study was conducted on diagnostic test accuracy reviews, which are more difficult to screen due to inconsistent terminology.