Neuromyelitis optica spectrum disorder (NMOSD), also known as Devic disease, is an autoimmune central nervous system disorder in humans that commonly causes inflammatory demyelination in the optic nerves and spinal cord. Inflammation in the optic nerves is termed optic neuritis (ON). ON is a common clinical presentation; however, it is not necessarily present in all NMOSD patients. ON in NMOSD can be relapsing and result in severe vision loss. To the best of our knowledge, no study utilises deep learning to classify ON changes on MRI among patients with NMOSD. Therefore, this study aims to deploy eight state-of-the-art CNN models (Inception-v3, Inception-ResNet-v2, ResNet-101, Xception, ShuffleNet, DenseNet-201, MobileNet-v2, and EfficientNet-B0) with transfer learning to classify NMOSD patients with and without chronic ON using optic nerve magnetic resonance imaging. This study also investigated the effects of data augmentation before and after dataset splitting on cropped and whole images. Both quantitative and qualitative assessments (with Grad-Cam) were used to evaluate the performances of the CNN models. The Inception-v3 was identified as the best CNN model for classifying ON among NMOSD patients, with accuracy of 99.5%, sensitivity of 98.9%, specificity of 93.0%, precision of 100%, NPV of 99.0%, and F1-score of 99.4%. This study also demonstrated that the application of augmentation after dataset splitting could avoid information leaking into the testing datasets, hence producing more realistic and reliable results.

OBJECTIVE: To assess the effect of propranolol on time to delivery among patients undergoing induction or augmentation of labor.
METHODS: PubMed, Scopus, Cochrane Library, ClinicalTrials.gov, and CINAHL (EBSCO) were searched from inception to December 2023.
METHODS: Randomized controlled trials (RCTs) that examined the impact of propranolol on time to delivery among patients undergoing induction or augmentation of labor were included. RCTs that included stillbirth before randomization, non-randomized trials, observational, cohort, case control, or studies in which the control group included an intervention other than standard care were excluded.
METHODS: Primary outcome was time to delivery after administration of propranolol among patients undergoing induction or augmentation of labor. The summary measures were reported as summary mean difference (MD) or relative risk with 95% of confidence interval (CI).
RESULTS: Nine RCTs including 1,182 patients were included in this meta-analysis. Five studies investigated the effect of propranolol among patients undergoing induction of labor (IOL) and demonstrated a significant decrease in time to delivery (MD, -91.5 minutes, 95% CI -110.6 to -72.4). Four studies investigated the effect of propranolol among patients undergoing augmentation of labor and showed no significant decrease in time to delivery (MD, -2.98 minutes, 95% CI -21.6 to 15.6). Our pooled analysis demonstrated that the use of propranolol in IOL and augmentation was associated with a decrease in time to delivery from administration of propranolol compared to placebo (mean difference, -46.15 minutes, 95% CI -59.48 to -32.81). The meta-analysis found no increased risk of PPH, blood transfusion, cesarean delivery rates, or NICU admission with the use of propranolol during labor.
CONCLUSIONS: The use of propranolol during induction of labor shortens overall time to delivery by about 91 minutes and did not significantly decrease time to delivery in those undergoing augmentation of labor.

UNASSIGNED: The search for an ideal bone substitute in reconstructive surgery has led to the exploration of various materials, with hydroxyapatite (HaP) emerging as a promising candidate due to its biocompatibility, osteoconductive properties, and structural similarity to human bone. Despite its potential, there is a paucity of data on the long-term safety and efficacy of HaP in facial skeletal reconstruction and augmentation.
UNASSIGNED: We conducted a systematic review following PRISMA 2020 guidelines, searching PubMed/MEDLINE, Google Scholar, CENTRAL, and Web of Science databases for studies on hydroxyapatite facial implants in reconstruction and augmentation.
UNASSIGNED: Our search yielded 12 studies that met our inclusion criteria, encompassing 74 patients treated with HaP implants for various indications including facial fractures/defects, aesthetic facial balancing, and after tumor resection. The studies reported on outcomes such as implant integration, complications, aesthetic results, and patient satisfaction, with a general trend indicating positive outcomes for the use of HaP in facial reconstruction.
UNASSIGNED: Hydroxyapatite appears to be a viable and effective material for facial skeletal reconstruction and augmentation, offering benefits in terms of biocompatibility, osteoconductivity, and patient outcomes. However, limitations such as low mechanical strength and the need for further research on long-term safety and efficacy were identified. This review underscores the potential of HaP in craniofacial surgery while highlighting areas for future investigation.

Many scales used in psychological and health research are designed to yield subscores, yet it is common to see total scores reported instead. One challenge of using subscores is they can lack adequate reliability due to their shortened length. However, methods originally developed for educational measurement have shown that augmenting subscores can improve reliability estimates. Augmented subscores blend the individual score with other sources of information. The present study sought to understand (a) the costs of ignoring subscores in favor of total scores and (b) the extent to which augmentation can help alleviate challenges encountered when using subscores. Data were simulated to examine when subscores should be preferred to total scores and the magnitude of improvement from using augmented subscores over non-augmented subscores. Results suggested that when a scale is designed to yield subscores, there is practical benefit to using them. In situations where subscore reliability is low, we recommend using augmentation.

UNASSIGNED: Restless legs syndrome (RLS) is a sensory-motor sleep disorder that affects up to 13% of adults in the Western world and 2-4% of children. It impairs night sleep with an impact on daily performances and life quality. Thus, moderate-to-severe RLS requires pharmacological treatment.
UNASSIGNED: In the present review, which is based on PubMed searches with no time limits, the authors discuss the recommended pharmacotherapy for RLS in addition to other emerging treatment options. The authors provide coverage to the current recommendations for both adults and pediatric patients with RLS.
UNASSIGNED: Current evidence suggests removing all causes of secondary RLS, including iron deficiency, chronic renal failure, drugs, and treating other sleep disorders that may worsen symptoms. Also, intermittent RLS should be addressed with behavioral measures and on-demand therapy. For chronic persistent RLS, α2δ calcium channel ligands are a first-line pharmacological approach, whereas dopamine agonists are associated with increased risk and should be spared. When RLS is refractory to first-line treatment, polytherapy, or opioid monotherapy should be considered. Nonetheless, some patients may not reach sustained symptom relief. Further research is needed to better understand the pathophysiology of RLS and to develop newer more effective drugs.

We propose strongly unrealistic data augmentation to improve the robustness of convolutional neural networks (CNNs) for automatic classification of dopamine transporter SPECT against the variability between sites and between cameras. Methods: A CNN was trained on a homogeneous dataset comprising 1,100 123I-labeled 2β-carbomethoxy-3β-(4-iodophenyl)-N-(3-fluoropropyl)nortropane SPECT images using strongly unrealistic data augmentation based on gaussian blurring and additive noise. Strongly unrealistic data augmentation was compared with no augmentation and intensity-based nnU-Net augmentation on 2 independent datasets with lower (n = 645) and considerably higher (n = 640) spatial resolution. Results: The CNN trained with strongly unrealistic augmentation achieved an overall accuracy of 0.989 (95% CI, 0.978-0.996) and 0.975 (95% CI, 0.960-0.986) in the independent test datasets, which was better than that without (0.960, 95% CI, 0.942-0.974; 0.953, 95% CI, 0.934-0.968) and with nnU-Net augmentation (0.972, 95% CI, 0.956-0.983; 0.950, 95% CI, 0.930-0.966) (all McNemar P < 0.001). Conclusion: Strongly unrealistic data augmentation results in better generalization of CNN-based classification of 123I-labeled 2β-carbomethoxy-3β-(4-iodophenyl)-N-(3-fluoropropyl)nortropane SPECT images to unseen acquisition settings. We hypothesize that this can be transferred to other nuclear imaging applications.

BACKGROUND: Artificial intelligence (AI) holds immense potential for enhancing clinical and administrative health care tasks. However, slow adoption and implementation challenges highlight the need to consider how humans can effectively collaborate with AI within broader socio-technical systems in health care.
OBJECTIVE: In the example of intensive care units (ICUs), we compare data scientists\' and clinicians\' assessments of the optimal utilization of human and AI capabilities by determining suitable levels of human-AI teaming for safely and meaningfully augmenting or automating 6 core tasks. The goal is to provide actionable recommendations for policy makers and health care practitioners regarding AI design and implementation.
METHODS: In this multimethod study, we combine a systematic task analysis across 6 ICUs with an international Delphi survey involving 19 health data scientists from the industry and academia and 61 ICU clinicians (25 physicians and 36 nurses) to define and assess optimal levels of human-AI teaming (level 1=no performance benefits; level 2=AI augments human performance; level 3=humans augment AI performance; level 4=AI performs without human input). Stakeholder groups also considered ethical and social implications.
RESULTS: Both stakeholder groups chose level 2 and 3 human-AI teaming for 4 out of 6 core tasks in the ICU. For one task (monitoring), level 4 was the preferred design choice. For the task of patient interactions, both data scientists and clinicians agreed that AI should not be used regardless of technological feasibility due to the importance of the physician-patient and nurse-patient relationship and ethical concerns. Human-AI design choices rely on interpretability, predictability, and control over AI systems. If these conditions are not met and AI performs below human-level reliability, a reduction to level 1 or shifting accountability away from human end users is advised. If AI performs at or beyond human-level reliability and these conditions are not met, shifting to level 4 automation should be considered to ensure safe and efficient human-AI teaming.
CONCLUSIONS: By considering the sociotechnical system and determining appropriate levels of human-AI teaming, our study showcases the potential for improving the safety and effectiveness of AI usage in ICUs and broader health care settings. Regulatory measures should prioritize interpretability, predictability, and control if clinicians hold full accountability. Ethical and social implications must be carefully evaluated to ensure effective collaboration between humans and AI, particularly considering the most recent advancements in generative AI.

BACKGROUND: The prevalence of osteoporosis is escalating alongside an aging global population, increasing the demand for spinal surgeries, including those necessitating cement augmentation for enhanced construct stability.
OBJECTIVE: This article delves into the nuanced application of cement augmentation techniques for pedicle screws and vertebral body replacements (VBR), aimed at optimizing surgical outcomes in osteoporotic spines.
METHODS: Drawing from a comprehensive literature review according to important clinical and biomechanical studies and the authors\' clinical experiences, we elucidate strategies to mitigate complications and improve surgical efficacy.
RESULTS: Cement augmentation has shown promise in managing vertebral fractures and in securing pedicle screws within osteoporotic vertebrae, with the advent of polymethylmethacrylate (PMMA) bone cement marking a pivotal advancement in spinal surgery. We highlight intraoperative measures like the choice between pre-injecting cement and utilizing cannulated or fenestrated screws, emphasizing the importance of controlling cement viscosity to prevent leakage and embolism. Through two case reports, we demonstrate the practical application of endplate cementation following VBR.
CONCLUSIONS: While the use of cement augmentation poses certain risks, its judicious application-supported by evidence-based guidelines and surgical expertise-can substantially enhance the stability of spinal constructs in osteoporotic patients. This allows a reduction in instrumentation length by enhancing biomechanical stability concerning pullout, bending, and rotational forces. Furthermore, the incidence of endplate sintering following VBF can be significantly reduced. Future research, particularly on antibiotic-loaded PMMA, may further expand its utility and optimize its safety profile.

We estimate relative hazards and absolute risks (or cumulative incidence or crude risk) under cause-specific proportional hazards models for competing risks from double nested case-control (DNCC) data. In the DNCC design, controls are time-matched not only to cases from the cause of primary interest, but also to cases from competing risks (the phase-two sample). Complete covariate data are available in the phase-two sample, but other cohort members only have information on survival outcomes and some covariates. Design-weighted estimators use inverse sampling probabilities computed from Samuelsen-type calculations for DNCC. To take advantage of additional information available on all cohort members, we augment the estimating equations with a term that is unbiased for zero but improves the efficiency of estimates from the cause-specific proportional hazards model. We establish the asymptotic properties of the proposed estimators, including the estimator of absolute risk, and derive consistent variance estimators. We show that augmented design-weighted estimators are more efficient than design-weighted estimators. Through simulations, we show that the proposed asymptotic methods yield nominal operating characteristics in practical sample sizes. We illustrate the methods using prostate cancer mortality data from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial Study of the National Cancer Institute.

UNASSIGNED: The current study aims to evaluate the correlation between oxytocin augmentation and postpartum hemorrhage.
UNASSIGNED: PubMed, Web of Science, and Scopus has been searched for studies assessing the correlation between oxytocin augmentation and postpartum hemorrhage up to January 24, 2024. The search strategy included relevant keywords related to PPH and oxytocin augmentation. The risk of bias assessment was conducted by two reviewers using the Newcastle-Ottawa Scale (NOS). To pool the effects sized of included studies odds ratios (OR) of interest outcome with their 95% confidence interval (CI) were used.
UNASSIGNED: Eight studies were included in this meta-analysis. The pooled analysis of the included studies showed a statistically significant association between oxytocin augmentation and increased odds of PPH (pooled odds ratio [OR] = 1.27, 95% confidence interval [CI]: 1.05-1.53; I2 = 84.94%; p = 0.01). Publication bias was assessed using funnel plots, which appeared relatively asymmetrical, indicating significant publication bias. Galbraith plot and trim and fill plot were used for publication bias. Sensitivity analyses were performed by leave one out method.
UNASSIGNED: This meta-analysis suggests that using oxytocin for labor augmentation is linked to a significant increase in the risk of PPH. It highlights the need for careful monitoring and consideration when using oxytocin, especially in low and middle-income countries where guidelines and supervision are crucial.