Tag: nevin manimala

Clin Spine Surg. 2021 Apr 19. doi: 10.1097/BSD.0000000000001184. Online ahead of print.

ABSTRACT

STUDY DESIGN: Retrospective review of spine surgery malpractice cases.

OBJECTIVES: The aim was to compare medical malpractice outcomes among different types of spine surgery and identify predictors of litigation outcomes.

SUMMARY OF BACKGROUND DATA: Spine surgery is highly litigious in the United States with data suggesting favorable outcomes for defendant surgeons. However, factor specific data and explanations for plaintiff verdicts are lacking.

METHODS: Westlaw legal database was queried for spine surgery malpractice outcomes from 2010 to 2019. Clinical data, reasons for litigation, and legal outcomes were tabulated. Statistical analysis was performed to identify factors associated with litigation outcomes.

RESULTS: A total of 257 cases were identified for inclusion. There were 98 noninstrumented and 148 instrumented cases; 110 single-level and 99 multilevel; 83 decompressions, 95 decompression and fusions, and 47 fusion only. In all, 182 (71%) resulted in a defendant verdict, 44 (17%) plaintiff verdict, and 31 (12%) settlement. Plaintiff verdicts resulted in payouts of $2.03 million, while settlements resulted in $1.11 million (P=0.34). Common reasons for litigation were intraoperative error, hardware complication, and improper postoperative management. Cases were more likely to result for the plaintiff if postoperative cauda equina syndrome (55% vs. 26%, P<0.01), a surgical site infection (46% vs. 27%, P=0.03), or other catastrophic injury (40% vs. 26%, P=0.03) occurred. Higher monetary awards were associated with multi versus single-level (median: $2.61 vs. $0.92 million, P=0.01), improper postoperative management cited (median: $2.29 vs. $1.12 million, P=0.04), and permanent neurological deficits ($2.29 vs. $0.78 million, P<0.01). Plaintiff payouts were more likely if defendant specialty was neurosurgery versus orthopedic surgery (33% vs. 18%, P=0.01).

CONCLUSIONS: Spine surgery is a litigious field with multiple factors associated with outcomes. Efforts to reduce intraoperative errors and complications may improve patient care and decrease the risk of litigation.

PMID:33872221 | DOI:10.1097/BSD.0000000000001184

PLoS Comput Biol. 2021 Apr 19;17(4):e1008054. doi: 10.1371/journal.pcbi.1008054. Online ahead of print.

ABSTRACT

Transfer entropy (TE) is a widely used measure of directed information flows in a number of domains including neuroscience. Many real-world time series for which we are interested in information flows come in the form of (near) instantaneous events occurring over time. Examples include the spiking of biological neurons, trades on stock markets and posts to social media, amongst myriad other systems involving events in continuous time throughout the natural and social sciences. However, there exist severe limitations to the current approach to TE estimation on such event-based data via discretising the time series into time bins: it is not consistent, has high bias, converges slowly and cannot simultaneously capture relationships that occur with very fine time precision as well as those that occur over long time intervals. Building on recent work which derived a theoretical framework for TE in continuous time, we present an estimation framework for TE on event-based data and develop a k-nearest-neighbours estimator within this framework. This estimator is provably consistent, has favourable bias properties and converges orders of magnitude more quickly than the current state-of-the-art in discrete-time estimation on synthetic examples. We demonstrate failures of the traditionally-used source-time-shift method for null surrogate generation. In order to overcome these failures, we develop a local permutation scheme for generating surrogate time series conforming to the appropriate null hypothesis in order to test for the statistical significance of the TE and, as such, test for the conditional independence between the history of one point process and the updates of another. Our approach is shown to be capable of correctly rejecting or accepting the null hypothesis of conditional independence even in the presence of strong pairwise time-directed correlations. This capacity to accurately test for conditional independence is further demonstrated on models of a spiking neural circuit inspired by the pyloric circuit of the crustacean stomatogastric ganglion, succeeding where previous related estimators have failed.

PMID:33872296 | DOI:10.1371/journal.pcbi.1008054

PLoS Comput Biol. 2021 Apr 19;17(4):e1008856. doi: 10.1371/journal.pcbi.1008856. Online ahead of print.

ABSTRACT

Convolutional Neural Networks (CNNs) are statistical models suited for learning complex visual patterns. In the context of Species Distribution Models (SDM) and in line with predictions of landscape ecology and island biogeography, CNN could grasp how local landscape structure affects prediction of species occurrence in SDMs. The prediction can thus reflect the signatures of entangled ecological processes. Although previous machine-learning based SDMs can learn complex influences of environmental predictors, they cannot acknowledge the influence of environmental structure in local landscapes (hence denoted “punctual models”). In this study, we applied CNNs to a large dataset of plant occurrences in France (GBIF), on a large taxonomical scale, to predict ranked relative probability of species (by joint learning) to any geographical position. We examined the way local environmental landscapes improve prediction by performing alternative CNN models deprived of information on landscape heterogeneity and structure (“ablation experiments”). We found that the landscape structure around location crucially contributed to improve predictive performance of CNN-SDMs. CNN models can classify the predicted distributions of many species, as other joint modelling approaches, but they further prove efficient in identifying the influence of local environmental landscapes. CNN can then represent signatures of spatially structured environmental drivers. The prediction gain is noticeable for rare species, which open promising perspectives for biodiversity monitoring and conservation strategies. Therefore, the approach is of both theoretical and practical interest. We discuss the way to test hypotheses on the patterns learnt by CNN, which should be essential for further interpretation of the ecological processes at play.

PMID:33872302 | DOI:10.1371/journal.pcbi.1008856

Int J Radiat Biol. 2021 Apr 19:1-5. doi: 10.1080/09553002.2021.1918972. Online ahead of print.

NO ABSTRACT

PMID:33872125 | DOI:10.1080/09553002.2021.1918972

IEEE Trans Biomed Eng. 2021 Apr 19;PP. doi: 10.1109/TBME.2021.3073833. Online ahead of print.

ABSTRACT

OBJECTIVE: While understanding the interaction patterns among simultaneous recordings of spike trains from multiple neuronal units is a key topic in neuroscience, existing methods either do not consider the inherent point-process nature of spike trains or are based on parametric assumptions. This work presents an information-theoretic framework for the model-free, continuous-time estimation of both undirected (symmetric) and directed (Granger-causal) interactions between spike trains.

METHODS: The framework computes the mutual information rate (MIR) and the transfer entropy rate (TER) for two point processes X and Y, showing that the MIR between X and Y can be decomposed as the sum of the TER along the directions X Y and Y X. We present theoretical expressions and introduce strategies to estimate efficiently the two measures through nearest neighbor statistics.

RESULTS: Using simulations of independent and coupled spike train processes, we show the accuracy of MIR and TER to assess interactions even for weakly coupled and short realizations, and prove the superiority of continuous-time estimation over the standard discrete-time approach. In a real data scenario of recordings from in-vitro preparations of spontaneously-growing cultures of cortical neurons, we show the ability of MIR and TER to describe how the functional organization of the networks of spike train interactions emerges through maturation of the neuronal cultures.

CONCLUSION AND SIGNIFICANCE: the proposed framework provides principled measures to assess undirected and directed spike train interactions with more efficiency and flexibility than previous discrete-time or parametric approaches, opening new perspectives for the analysis of point-process data in neuroscience and many other fields.

PMID:33872139 | DOI:10.1109/TBME.2021.3073833

J Med Internet Res. 2021 Apr 17. doi: 10.2196/28973. Online ahead of print.

ABSTRACT

BACKGROUND: On the 30th of January 2020, the WHO’s Emergency Committee declared the rapid worldwide spread of COVID-19, a global health emergency. Since then tireless efforts have been made to mitigate the spread of the disease and its impact, mostly relying on non-pharmaceutical interventions. By December 2020, the safety and efficacy of the first COVID-19 vaccines have been demonstrated. Lately, the large social media platform Twitter has been utilized by medical research for the analysis of important public health topics, such as the publics´ perception on antibiotic use and misuse and human papillomavirus vaccination. Analysis of Twitter-generated data can be further facilitated by utilizing the inbuilt, anonymous, polling tool, in order to gain insight into public health issues with rapid feedback on an international scale. During the fast-paced course of the COVID-19 pandemic the Twitter polling system offers a viable method to gain rapid large-scale international public health insights on highly relevant and timely SARS-CoV-2 related topics.

OBJECTIVE: The purpose of this study was to understand the public’s perception on the safety and acceptance of the COVID-19 vaccines in real-time through Twitter polls.

METHODS: Two Twitter polls were developed to explore the public’s views on the currently available COVID-19 vaccines. The surveys were pinned to the Digital Health and Patient Safety Platform Twitter timeline for one week in mid-February 2021 and Twitter users and influencers were asked to participate and re-tweet the polls to reach the largest possible audience.

RESULTS: Adequacy of COVID-19 vaccine safety (of currently available vaccines; Poll 1) was agreed upon by 1,579 out of 3,439 (45.9%) Twitter users, in contrast to almost as many Twitter users (n=1,434/3,439; 41.7%) being unsure about their safety. Only 5.2% (179/3,439) rated the currently available COVID-19 vaccines as generally unsafe. Poll 2, addressing the question whether users would get vaccinated, was answered affirmatively by 82.8% (2,862/3,457) and only 8% (277/3,457) categorically rejected vaccination at the time of polling.

CONCLUSIONS: In contrast to the perceived high level of uncertainty about the safety of currently available COVID-19 vaccines, there is an elevated willingness to get vaccinated among this study sample. Since people’s perceptions and views are strongly influenced by the (social) media, snapshots provided from these media represent a static image of a moving target. Thus, the results of this work need to be followed by long-term surveys in an effort to keep validity. This is especially relevant under the circumstances of a fast-paced pandemic course, in order not to miss sudden rises of hesitancy, which may have detrimental effects on the pandemics course.

PMID:33872185 | DOI:10.2196/28973

Stat Methods Med Res. 2021 Apr 19:9622802211002868. doi: 10.1177/09622802211002868. Online ahead of print.

ABSTRACT

Joint modeling of zero-inflated count and time-to-event data is usually performed by applying the shared random effect model. This kind of joint modeling can be considered as a latent Gaussian model. In this paper, the approach of integrated nested Laplace approximation (INLA) is used to perform approximate Bayesian approach for the joint modeling. We propose a zero-inflated hurdle model under Poisson or negative binomial distributional assumption as sub-model for count data. Also, a Weibull model is used as survival time sub-model. In addition to the usual joint linear model, a joint partially linear model is also considered to take into account the non-linear effect of time on the longitudinal count response. The performance of the method is investigated using some simulation studies and its achievement is compared with the usual approach via the Bayesian paradigm of Monte Carlo Markov Chain (MCMC). Also, we apply the proposed method to analyze two real data sets. The first one is the data about a longitudinal study of pregnancy and the second one is a data set obtained of a HIV study.

PMID:33872092 | DOI:10.1177/09622802211002868

J Biophotonics. 2021 Apr 19. doi: 10.1002/jbio.202100058. Online ahead of print.

ABSTRACT

Understanding and amelioration of the effects of solar radiation exposure are critical in preventing the occurrence of skin cancer. Towards this end, many studies have been conducted in 2D cell culture models under simplified and unrealistic conditions. 3D culture models better capture the complexity of in vivo physiology, although the effects of the 3D extracellular matrix have not been well studied. Monitoring the instantaneous and resultant cellular responses to exposure, and the influence of the 3D environment, could provide an enhanced understanding of the fundamental processes of photocarcinogenesis. This work presents an analysis of the biochemical impacts of simulated solar radiation (SSR) occurring in immortalised human epithelial keratinocytes (HaCaT), in a 3D skin model, compared to 2D culture. Cell viability was monitored using the Alamar Blue colorometric assay (AB), and the impact of the radiation exposure, at the level of the biomolecular constituents (nucleic acids and proteins), were evaluated through the combination of Raman microspectroscopy and multivariate statistical analysis. The results suggest that SSR exposure induces alterations of the conformational structure of DNA as an immediate impact, whereas changes in the protein signature are primarily seen as a subsequent response. This article is protected by copyright. All rights reserved.

PMID:33871950 | DOI:10.1002/jbio.202100058

Biom J. 2021 Apr 19. doi: 10.1002/bimj.202000240. Online ahead of print.

ABSTRACT

We propose a mathematical model based on probability theory to optimize COVID-19 testing by a multistep batch testing approach with variable batch sizes. This model and simulation tool dramatically increase the efficiency and efficacy of the tests in a large population at a low cost, particularly when the infection rate is low. The proposed method combines statistical modeling with numerical methods to solve nonlinear equations and obtain optimal batch sizes at each step of tests, with the flexibility to incorporate geographic and demographic information. In theory, this method substantially improves the false positive rate and positive predictive value as well. We also conducted a Monte Carlo simulation to verify this theory. Our simulation results show that our method significantly reduces the false negative rate. More accurate assessment can be made if the dilution effect or other practical factors are taken into consideration. The proposed method will be particularly useful for the early detection of infectious diseases and prevention of future pandemics. The proposed work will have broader impacts on medical testing for contagious diseases in general.

PMID:33871898 | DOI:10.1002/bimj.202000240

Stress Health. 2021 Mar 26. doi: 10.1002/smi.3045. Online ahead of print.

ABSTRACT

Education actively helps us develop our well-being and health, but postgraduate students are at high risk of depression. The prevalence of depression symptoms varies from 6.2% to 84.7% among them, and its changes throughout the years remains unclear. The present study aimed to estimate the real prevalence of depression symptoms among postgraduate students and the changes from 1980 to 2020. Thirty-seven primary studies with 41 independent reports were included in the meta-analysis (none reports were in high-quality, three were medium-to-high quality, 20 were low-to-medium quality, and 18 were low-quality), involving 27,717 postgraduate students. The pooled prevalence of overall, mild, moderate, and severe depression symptoms was 34% (95% CI: 28-40, I² = 98.6%), 27% (95% CI: 22-32, I² = 85.8%), 13% (95% CI: 8-21, I² = 97.3%), and 8% (95% CI: 6-11, I² = 81.0%), respectively. Overall, the prevalence of depression symptoms remained relatively constant through the years following 1980 (overall: β = -0.12, 95% CI: [-0.39, 0.15], p = 0.39; mild: β = 0.24, 95% CI: [-0.02, 0.51], p = 0.07; moderate: β = -0.24, 95% CI: [-0.75, 0.26], p = 0.34; severe: β = 0.13, 95% CI: [-0.25, 0.51], p = 0.50). Doctoral students experienced more depressive symptoms than did master’s students (43% vs. 27%; Q = 2.23, df = 1, p = 0.13), and studies utilising non-random sampling methods reported a higher prevalence of mild depression and lower moderate depression symptoms than those that used random sampling (overall: 34% vs. 29%; Q = 0.45, df = 1, p = 0.50; mild: 29% vs. 21%; Q = 1.69, df = 1, p = 0.19; moderate: 16% vs. 25%; Q = 1.79, df = 1, p = 0.18; severe: 8% vs. 9%; Q = 0.13, df = 1, p = 0.72) despite these differences was not statistically significant. The prevalence of depression symptoms was moderated by the measurements and the quality of primary studies. More than one-third of postgraduates reported depression symptoms, which indicates the susceptibility to mental health risk among postgraduates. School administrators, teachers, and students should take joint actions to prevent mental disorders of postgraduates from increasing in severity.

PMID:33871902 | DOI:10.1002/smi.3045