Background:In this meta-analysis, we explore the role of repetitive transcranial magnetic stimulation (rTMS), a noninvasive neuromodulation technique in the treatment of chronic pain.Methods:Studies comparing rTMS and conventional treatment for chronic pain were searched. The comparison was made for decrease in the pain scores with and without (sham) the use of rTMS after a follow-up interval of 4–8 weeks. All reported pain scores were converted into a common scale ranging from “0” (no pain) to “10” (worst pain).Results:Nine trials with 183 patients in each of the groups were included in the analysis. The decrease in pain scores with rTMS was 1.12 (95% confidence interval [CI] being 1.46–0.78) (fixed effects, I2 = 0%, P < 0.001) and in sham-rTMS was 0.28 (95% CI being 0.49–0.07) (Fixed effects, I2 = 0, P = 0.01). The pooled mean drop in pain scores with rTMS therapy was higher by 0.79 (95% CI being 0.26–1.33) (fixed effects, I2 = 0, P < 0.01). The duration and frequency of rTMS were highly variable across trials. Publication bias was unlikely (Egger's test, X-intercept = 0.13, P = 0.75).Conclusions:Use of rTMS improves the efficacy of conventional medical treatment in chronic pain patients. This treatment is not associated with any direct adverse effects. However, the duration and frequency of rTMS therapy is presently highly variable and needs standardization.

Background:We hypothesize that being an editorial board member (EBM) in a high impact factor specialty medical journal increases the chances of publishing in the same journal.Materials and Methods:The publication trends of the first five EBMs in the five highest impact factor Anesthesiology and Gastroenterology journals were analyzed. Preceding 5 years' publications appearing on PubMed were grouped into as follows: number of publications in the journal in which the EBM serves (N1), number of publications by the same author in the other four highest impact factor (IF) journals (N2) and number of publications in all the other journals (N3). We evaluated the probability of the observed distribution of publications in the five highest IF journals happening by chance alone, assuming that all the EBMs had the same opportunity of publishing in any of these journals. The probability of publishing in their own journal was assumed to be one fifth.Results:The EBMs published their manuscripts in their own journal at a very high frequency. Encompassing all ten journals, the calculated P value for such a distribution was <0.001. In two journals, Anesthesia and Analgesia and Anaesthesia, the EBMs' publications in their journal were more than twice the cumulative total in the remaining four journals. In three of the five gastroenterology journals analyzed, combined publications of the five EBMs were greater in their own journal than the remaining four journals combined.Conclusions:Despite proclaimed fair peer review process, EBMs seem to get preference in their own journals.

BackgroundOpioid medications are commonly used to treat chronic pain around the world. While these medications are quite effective at reducing pain, they can create opioid dependence and lead to further drug addiction. Longterm opioid use has significantly contributed to the "opioid epidemic" that is currently ravaging the United States, leading to opioid overdoses and unintentional deaths, particularly in Delaware. ObjectiveTo determine if medical marijuana certification helps patients in Delaware with chronic pain reduce their opiate use. MethodsIn this study, we examined individuals who were provided with legal; medical cannabis certifications in the state of Delaware between June 2018 and October 2019 and were concurrently being treated with opioid medications for chronic pain at a private pain management practice. Using a posthoc analysis, we conducted a retrospective cohort study on the individuals (n = 81) to determine if there was a decrease in their opioid use following medical cannabis certification. Opioid use was measured in morphine milligram equivalent (MME) through the Delaware prescription monitoring program (PMP) database. ResultsOverall, the average change in prescribed opioid use was found to be -12.3 morphine milligram equivalent (MME) units when including all individuals (p < 0.00001). Among the included individuals with baseline opioid use, medical cannabis certification was associated with a 31.3% average decrease in opioid use (n = 63). When examining subgroups based upon pain location, individuals with neck pain displayed a 41.5% average decrease in MME (n = 27), while individuals with low back pain were observed to have a 29.4% decrease in opioid use (n = 58). Similarly, individuals with knee pain (n = 14) reduced their opioid use by 32.6%. ConclusionThe results display an association between medical cannabis certification and a decrease in opiate use among the study group individuals. This study suggests that medical cannabis use may help individuals to reduce their opiate requirements along with physician intervention. More research is needed to validate these findings with appropriate controls and verification of cannabis use.

INTRODUCTION:Automated video-based feedback for a surgeon’s technical skills would improve patient care and facilitate trainee education, but current feedback methods require the development of task-specific metrics. A generalizable, automated method of surgeon skills assessment with surgeon tool-usage signatures could expedite the development and validation of video-based feedback methods.METHODS:Annotated images from the publicly-available SOCAL dataset were used to train an off-the-shelf computer vision model (YOLOv4) which detected surgical instruments. Shannon entropy of unique instruments combinations was calculated from each surgical trial. Logistic regression was used to predict trial success using Shannon entropy.RESULTS:Surgeon signatures based on Shannon entropy were created for each trial and each surgeon. Instrument usage patterns demonstrated differences between successful and unsuccessful trials. Shannon entropy of instrument combinations demonstrated significant correlation with trial success (p < 0.001) and predicted success with 97% average precision and 78% accuracy using computer vision detections. Unsuccessful trials displayed a rapid initial peak in entropy which then declined over time. In contrast, successful trials demonstrated slower progression of tool usage with gradually increasing use of a critical, final maneuver.CONCLUSIONS:Surgeon signatures based on Shannon entropy predicted task success with acceptable accuracy and revealed patterns between successful and unsuccessful trials. Shannon entropy offers a generalizable and summative signal about surgeon performance, regardless of the task, and can predict outcomes based only on the probabilities of unique instrument combinations. Future efforts will describe the instrument movement signatures.