IntroductionVaginismus is mostly unknown among clinicians and women. Vaginismus causes women to have fear, anxiety, and pain with penetration attempts.AimTo present a large cohort of patients based on prior published studies approved by an institutional review board and the Food and Drug Administration using a comprehensive multimodal vaginismus treatment program to treat the physical and psychologic manifestations of women with vaginismus and to record successes, failures, and untoward effects of this treatment approach.MethodsAssessment of vaginismus included a comprehensive pretreatment questionnaire, the Female Sexual Function Index (FSFI), and consultation. All patients signed a detailed informed consent. Treatment consisted of a multimodal approach including intravaginal injections of onabotulinumtoxinA (Botox) and bupivacaine, progressive dilation under conscious sedation, indwelling dilator, follow-up and support with office visits, phone calls, e-mails, dilation logs, and FSFI reports.Main Outcome MeasuresLogs noting dilation progression, pain and anxiety scores, time to achieve intercourse, setbacks, and untoward effects. Post-treatment FSFI scores were compared with preprocedure scores.ResultsOne hundred seventy-one patients (71%) reported having pain-free intercourse at a mean of 5.1 weeks (median = 2.5). Six patients (2.5%) were unable to achieve intercourse within a 1-year period after treatment and 64 patients (26.6%) were lost to follow-up. The change in the overall FSFI score measured at baseline, 3 months, 6 months, and 1 year was statistically significant at the 0.05 level. Three patients developed mild temporary stress incontinence, two patients developed a short period of temporary blurred vision, and one patient developed temporary excessive vaginal dryness. All adverse events resolved by approximately 4 months. One patient required retreatment followed by successful coitus.ConclusionA multimodal program that treated the physical and psychologic aspects of vaginismus enabled women to achieve pain-free intercourse as noted by patient communications and serial female sexual function studies. Further studies are indicated to better understand the individual components of this multimodal treatment program.Pacik PT, Geletta S. Vaginismus Treatment: Clinical Trials Follow Up 241 Patients. Sex Med 2017;5:e114–e123.

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Over the last 15 years, research on canid cognition has revealed that domestic dogs possess a surprising array of complex sociocognitive skills pointing to the possibility that the domestication process might have uniquely altered their brains; however, we know very little about how evolutionary processes (natural or artificial) might have modified underlying neural structure to support species‐specific behaviors. Evaluating the degree of cortical folding (i.e., gyrification) within canids may prove useful, as this parameter is linked to functional variation of the cerebral cortex. Using quantitative magnetic resonance imaging to investigate the impact of domestication on the canine cortical surface, we compared the gyrification index (GI) in 19 carnivore species, including six wild canid and 13 domestic dog individuals. We also explored correlations between global and local GI with brain mass, cortical thickness, white and gray matter volume and surface area. Our results indicated that GI values for domestic dogs are largely consistent with what would be expected for a canid of their given brain mass, although more variable than that observed in wild canids. We also found that GI in canids is positively correlated with cortical surface area, cortical thickness and total cortical gray matter volumes. While we found no evidence of global differences in GI between domestic and wild canids, certain regional differences in gyrification were observed.

BackgroundThis study used natural language processing (NLP) and machine learning (ML) techniques to identify reliable patterns from within research narrative documents to distinguish studies that complete successfully, from the ones that terminate. Recent research findings have reported that at least 10 % of all studies that are funded by major research funding agencies terminate without yielding useful results. Since it is well-known that scientific studies that receive funding from major funding agencies are carefully planned, and rigorously vetted through the peer-review process, it was somewhat daunting to us that study-terminations are this prevalent. Moreover, our review of the literature about study terminations suggested that the reasons for study terminations are not well understood. We therefore aimed to address that knowledge gap, by seeking to identify the factors that contribute to study failures.MethodWe used data from the clinicialTrials.gov repository, from which we extracted both structured data (study characteristics), and unstructured data (the narrative description of the studies). We applied natural language processing techniques to the unstructured data to quantify the risk of termination by identifying distinctive topics that are more frequently associated with trials that are terminated and trials that are completed. We used the Latent Dirichlet Allocation (LDA) technique to derive 25 “topics” with corresponding sets of probabilities, which we then used to predict study-termination by utilizing random forest modeling. We fit two distinct models – one using only structured data as predictors and another model with both structured data and the 25 text topics derived from the unstructured data.ResultsIn this paper, we demonstrate the interpretive and predictive value of LDA as it relates to predicting clinical trial failure. The results also demonstrate that the combined modeling approach yields robust predictive probabilities in terms of both sensitivity and specificity, relative to a model that utilizes the structured data alone.ConclusionsOur study demonstrated that the use of topic modeling using LDA significantly raises the utility of unstructured data in better predicating the completion vs. termination of studies. This study sets the direction for future research to evaluate the viability of the designs of health studies.

The types of services that are identified by consumers are standardized to typologies that are traditionally used in health service research. Five types of services were targeted - general practice physician offices, physician specialty services, dentists, hospitals and physical therapy services. The "five-star" rating systems were re-coded to form a five-point ordinal scale variable to represent "satisfaction score". Findings The Yelp! data-based measurement of patient satisfaction produced an overall satisfaction score of 3.8 (SD=1.7) for the sampled services. The average satisfaction score per type of service ranged from 3.16 (SD=1.83) for specialty physicians to 4.52 (SD=1.57) for physical therapists. In general, dentists and physical therapists received higher average satisfaction scores as compared to the other medical services. Research limitations/implications Because this study was meant to evaluate the utility of social media generated data to measure satisfaction, in general, the estimates cannot be construed as representative of any underlying geographically defined population. They, however, do have a "cohort" interpretability. This limitation is not inherent to the use of the data source. If some geographically identifiable representation of the measurement data is desired, identifiable business data can be generated from the Yelp! system to specifically target relevant populations following the method that are tested in this study. Practical implications Under certain circumstances, such as the size and maturity of the gathered data, social media generated data can be a useful as a "fortuitous" alternative to satisfaction surveys for evaluating patient satisfaction with medical care. This is propitious as there have been some indication by studies that the advent of communication media in the twenty-first century may be undermining the reliability of scientifically designed surveys. Originality/value The use of social media generated data as "alternative" or "secondary" data source for research use is currently being widely investigated. To the author's knowledge, this is the only paper that evaluated the use of "Yelp!" data as a possible source for population-based formal satisfaction measurement for healthcare services.