Evidence for use of a healthy relationships assessment tool in the CHARISMA pilot study

Introduction The CHARISMA intervention, nested within the MTN-025/HOPE vaginal ring trial in Johannesburg, South Africa, seeks to facilitate women’s use of HIV prevention products by promoting partner dialogue and mitigating intimate partner violence (IPV). We developed “HEART”, a lay counselor-administered relationship assessment tool, for the CHARISMA pilot. The five-scale tool assesses participants’ endorsement of Traditional Values (TV), her HIV Prevention Readiness (HPR) and levels of partner support (PS), abuse and control (PAC), and resistance to HIV prevention (PR), guiding decisions about which of three counselling modules to offer (partner communication/A; ring disclosure/B; and IPV prevention/C). Methods We correlated baseline scores on HEART subscales with a) independent measures of relationship stability, disclosure and IPV to assess construct validity, and b) with specific modules offered to determine how HEART was used in the pilot. We examined changes in HEART scores at three and six months. Finally, we ran separate growth models for each subscale to examine changes in scores, accounting for partnership changes and counseling module(s) received. Results Baseline HEART scores correlated as predicted among subscales and with other measures. Reliabilities for four subscales were 0.75 or higher. Women who disclosed study participation and ring use scored higher on PS and lower on PR. Women experiencing IPV scored lower on PS, and higher on PAC and PR. During the pilot, 82% of women received one and 17% received two or more modules; over half received the IPV module. Women with higher PAC and PR scores were more likely to receive the IPV than the communication or disclosure modules. Over time, the TV, PAC and PR scores decreased, and PS score increased. Receiving the IPV module was associated with a decreased PAC score. Conclusions These data offer preliminary evidence for HEART construct and predictive validity and support its further evaluation to guide implementation and monitor the impact of the CHARISMA intervention in a randomized controlled evaluation.

Augmented reality visualization in brain lesions: a prospective randomized controlled evaluation of its potential and current limitations in navigated microneurosurgery

Background Augmented reality (AR) has the potential to support complex neurosurgical interventions by including visual information seamlessly. This study examines intraoperative visualization parameters and clinical impact of AR in brain tumor surgery. Methods Fifty-five intracranial lesions, operated either with AR-navigated microscope (n = 39) or conventional neuronavigation (n = 16) after randomization, have been included prospectively. Surgical resection time, duration/type/mode of AR, displayed objects (n, type), pointer-based navigation checks (n), usability of control, quality indicators, and overall surgical usefulness of AR have been assessed. Results AR display has been used in 44.4% of resection time. Predominant AR type was navigation view (75.7%), followed by target volumes (20.1%). Predominant AR mode was picture-in-picture (PiP) (72.5%), followed by 23.3% overlay display. In 43.6% of cases, vision of important anatomical structures has been partially or entirely blocked by AR information. A total of 7.7% of cases used MRI navigation only, 30.8% used one, 23.1% used two, and 38.5% used three or more object segmentations in AR navigation. A total of 66.7% of surgeons found AR visualization helpful in the individual surgical case. AR depth information and accuracy have been rated acceptable (median 3.0 vs. median 5.0 in conventional neuronavigation). The mean utilization of the navigation pointer was 2.6 × /resection hour (AR) vs. 9.7 × /resection hour (neuronavigation); navigation effort was significantly reduced in AR (P < 0.001). Conclusions The main benefit of HUD-based AR visualization in brain tumor surgery is the integrated continuous display allowing for pointer-less navigation. Navigation view (PiP) provides the highest usability while blocking the operative field less frequently. Visualization quality will benefit from improvements in registration accuracy and depth impression. German clinical trials registration number. DRKS00016955.

Remote activity monitoring for family caregivers of persons living with dementia: a mixed methods, randomized controlled evaluation

Background The goal of the present study was to determine whether a remote activity monitoring (RAM) system benefited caregivers who aided relatives with Alzheimer’s disease or related dementias (ADRD) living at home. We hypothesized that over 18 months, families randomly assigned to receive RAM technology in the home of the person with ADRD would experience statistically significant (p < .05): 1) improvements in caregiver self-efficacy and sense of competence when managing their relative’s dementia; and 2) reductions in caregiver distress (e.g., burden, role captivity, and depression). Methods An embedded mixed methods design was utilized, where 179 dementia caregivers were randomly assigned to receive RAM or not. Caregivers were surveyed bi-annually over an 18-month period to collect quantitative and qualitative data on RAM’s effects. Semi-structured interviews with 30 caregivers were completed following the 18-month data collection period to explore more in-depth how and why RAM was perceived as helpful or not. Results Growth curve models showed no direct or moderation effect of RAM on dementia caregiver outcomes. The qualitative data revealed a complex utilization process of RAM influenced by the care environment/context as well as the temporal progression of ADRD and the caregiving trajectory. Conclusions The findings suggest the need for developing more effective mechanisms to match appropriate technologies with the heterogeneous needs and care contexts of people living with ADRD and their caregivers. A triadic approach that incorporates professional care management alongside passive monitoring systems such as RAM may also enhance potential benefits. Trial registration ClinicalTrials.govNCT03665909, retrospectively registered on 11 Sept 2018.

The impact of text presentation on translator performance

Widely used computer-aided translation (CAT) tools divide documents into segments, such as sentences, and arrange them side-by-side in a spreadsheet-like view. We present the first controlled evaluation of these design choices on translator performance, measuring speed and accuracy in three experimental text-processing tasks. We find significant evidence that sentence-by-sentence presentation enables faster text reproduction and within-sentence error identification compared to unsegmented text, and that a top-and-bottom arrangement of source and target sentences enables faster text reproduction compared to a side-by-side arrangement. For revision, on the other hand, we find that presenting unsegmented text results in the highest accuracy and time efficiency. Our findings have direct implications for best practices in designing CAT tools.

Cardiovascular Events in People With Type 2 Diabetes: Performance of Framingham, UKPDS, and ADVANCE Risk Equations

The aim of this study was to assess the performance of the Framingham, UK Prospective Diabetes Study (UKPDS), and the Action in Diabetes and Vascular disease: Preterax and Diamicron-MR Controlled Evaluation (ADVANCE) risk equations in the prediction of 4-year cardiovascular disease CVD) in Iranian people with type 2 diabetes. The 4-year risks of CVD were estimated using the three equations in a community of 557 patients with type 2 diabetes and free of CVD at baseline. A trained physician evaluated all of the participants regarding the occurrence of CVD events during follow-up. CVD was defined as major events including fatal/non-fatal myocardial infarction as well as fatal/non-fatal stroke, minor events including treated coronary heart disease (CHD), and established peripheral arterial disease (PAD). During four years of follow-up, 64 CVD events were observed (66% minor CVD events). Despite having a good calibration (estimated to observed ratio ranging from 91.37 to 98.2 percent, Hosmer–Lemeshow χ2 (HLχ2) values <15), both general (Framingham) and diabetes-specific (UKPDS and ADVANCE) equations did not have adequate discriminative ability (Area Under the Curve (AUC) ranging from 0.48 to 0.56). Framingham, UKPDS, and ADVANCE risk equations, regardless of being general or diabetes-specific, could not precisely predict 4-year risk of CVD in Iranian individuals with type 2 diabetes.

Controlled evaLuation of Angiotensin Receptor Blockers for COVID-19 respIraTorY disease (CLARITY): Statistical analysis plan for a randomised controlled Bayesian adaptive sample size trial

The CLARITY trial (Controlled evaLuation of Angiotensin Receptor Blockers for COVID-19 respIraTorY Disease) investigates the effectiveness of angiotensin receptor blockers in addition to standard care compared to placebo (in Indian sites) with standard care in reducing the duration and severity of lung failure in patients with COVID-19. The CLARITY trial is a multi-centre, randomised controlled Bayesian adaptive trial with regular planned analyses where pre-specified decision rules will be assessed to determine whether the trial should be stopped due to sufficient evidence of treatment effectiveness or futility. Here we describe the statistical analysis plan for the trial, and define the pre-specified decision rules, including those that could lead to the trial being halted. The primary outcome is clinical status on a 7-point ordinal scale adapted from the WHO Clinical Progression scale assessed at Day 14. The primary analysis will follow the intention-to-treat principle. A Bayesian adaptive trial design was selected because there is considerable uncertainty about the extent of potential benefit of this treatment.

An Adversarial Deep-Learning-Based Model for Cervical Cancer CTV Segmentation With Multicenter Blinded Randomized Controlled Validation

PurposeTo propose a novel deep-learning-based auto-segmentation model for CTV delineation in cervical cancer and to evaluate whether it can perform comparably well to manual delineation by a three-stage multicenter evaluation framework.MethodsAn adversarial deep-learning-based auto-segmentation model was trained and configured for cervical cancer CTV contouring using CT data from 237 patients. Then CT scans of additional 20 consecutive patients with locally advanced cervical cancer were collected to perform a three-stage multicenter randomized controlled evaluation involving nine oncologists from six medical centers. This evaluation system is a combination of objective performance metrics, radiation oncologist assessment, and finally the head-to-head Turing imitation test. Accuracy and effectiveness were evaluated step by step. The intra-observer consistency of each oncologist was also tested.ResultsIn stage-1 evaluation, the mean DSC and the 95HD value of the proposed model were 0.88 and 3.46 mm, respectively. In stage-2, the oncologist grading evaluation showed the majority of AI contours were comparable to the GT contours. The average CTV scores for AI and GT were 2.68 vs. 2.71 in week 0 (P = .206), and 2.62 vs. 2.63 in week 2 (P = .552), with no significant statistical differences. In stage-3, the Turing imitation test showed that the percentage of AI contours, which were judged to be better than GT contours by ≥5 oncologists, was 60.0% in week 0 and 42.5% in week 2. Most oncologists demonstrated good consistency between the 2 weeks (P &gt; 0.05).ConclusionsThe tested AI model was demonstrated to be accurate and comparable to the manual CTV segmentation in cervical cancer patients when assessed by our three-stage evaluation framework.