Evaluation of Quadratus Lumborum Block as Part of an Opioid-Free Anaesthesia for Canine Ovariohysterectomy

Quadratus lumborum block (QLB) is used to provide analgesia for abdominal surgery in humans. The aim of this study was to assess an anaesthetic protocol involving the QLB for canine ovariohysterectomy. Ten dogs were included. Anaesthetic protocol consisted of premedication with IM medetomidine (20 μg kg−1) and SC meloxicam (0.1 mg kg−1), induction with propofol to effect, and maintenance with sevoflurane in oxygen/medical air. QLB was performed injecting 0.4 mL kg−1 of 0.25% bupivacaine/iohexol per side. Computed Tomography (CT) was performed before and after surgery. Fentanyl was administered as rescue analgesia during surgery. The Short Form of The Glasgow Composite Pain Scale and thermal threshold (TT) at the level of the elbow, T10, T13 and L3 were assessed before premedication and every hour postoperatively. Methadone was given as rescue analgesia postoperatively when pain score was >3. A Yuen’s test on trimmed means for dependent samples was used to analyse the data (p < 0.05). CT images showed spreading of the contrast/block for a median (range) of 3 (2–5) vertebrae, without differences between preoperative and postoperative images. One dog needed rescue analgesia during surgery. Pain score was less than 4/24 in all the animals during the first 4 h after surgery. TT showed a significant increased signal in all the areas tested, apart from the humerus, 30 min after surgery. The QLB may provide additional analgesia for canine ovariohysterectomy. Further studies are needed to assess the specific contribution of the QLB in abdominal analgesia.

Self-regulated learning, academic achievement and socioeconomic context at the end of primary school

The increase of inequalities and the learning crisis due to COVID-19 pandemic has forced to review the role of education in the attainment of skills to learn throughout life. The purpose of this study is to investigate the incidence of the academic achievement on selfregulation strategies (forethought, inhibition and volitional inhibition), considering the socioeconomical context at the end of elementary school. The SRL strategies are assessed, from the perspective of students and teachers, triangulating measurement in different tasks. 67 students in their last year of primary education participated. The SRL measures were compared using robust tests considering high and low academic achievement and low and medium socioeconomic context (robust version of Welch’s test for two groups, Yuen’s test, and two-way ANOVA based on trimmed means and Winsorized variances). The academic achievement affects and significantly predicts the forethought strategy. In the low socioeconomical context, the students with a high academic achievement maximize their SRL. The modulating role of the school experience in self-regulation is discussed.

Sensitivity to missing not at random dropout in clinical trials: use and interpretation of the Trimmed Means Estimator

Outcome values in randomized controlled trials (RCTs) may be missing not at random (MNAR), if patients with extreme outcome values are more likely to drop out (e.g., due to perceived ineffectiveness of treatment, or adverse effects). In such scenarios, estimates from complete case analysis (CCA) and multiple imputation (MI) will be biased. The trimmed means (TM) estimator operates by setting missing values to the most extreme value, and then “trimming” away equal fractions of both treatment groups, estimating the treatment effect using the remaining data. The TM estimator relies on two assumptions, which we term the “strong MNAR” and “location shift” assumptions. In this article, we derive formulae for the bias resulting from the violation of these assumptions for normally distributed outcomes. We propose an adjusted estimator, which relaxes the location shift assumption and detail how our bias formulae can be used to establish the direction of bias of CCA, MI and TM estimates under a range of plausible data scenarios, to inform sensitivity analyses. The TM approach is illustrated with simulations and in a sensitivity analysis of the CoBalT RCT of cognitive behavioural therapy (CBT) in 469 individuals with 46 months follow-up. Results were consistent with a beneficial CBT treatment effect. The MI estimates are closer to the null than the CCA estimate, whereas the TM estimate was further from the null. We propose using the TM estimator as a sensitivity analysis for data where it is suspected that extreme outcome values are missing.

The Percentile Bootstrap: A Primer With Step-by-Step Instructions in R

The percentile bootstrap is the Swiss Army knife of statistics: It is a nonparametric method based on data-driven simulations. It can be applied to many statistical problems, as a substitute to standard parametric approaches, or in situations for which parametric methods do not exist. In this Tutorial, we cover R code to implement the percentile bootstrap to make inferences about central tendency (e.g., means and trimmed means) and spread in a one-sample example and in an example comparing two independent groups. For each example, we explain how to derive a bootstrap distribution and how to get a confidence interval and a p value from that distribution. We also demonstrate how to run a simulation to assess the behavior of the bootstrap. For some purposes, such as making inferences about the mean, the bootstrap performs poorly. But for other purposes, it is the only known method that works well over a broad range of situations. More broadly, combining the percentile bootstrap with robust estimators (i.e., estimators that are not overly sensitive to outliers) can help users gain a deeper understanding of their data than they would using conventional methods.

The percentile bootstrap: a primer with step-by-step instructions in R

The percentile bootstrap is the Swiss Army Knife of statistics: it is a non-parametric method based on data-driven simulations. It can be applied to many statistical problems, as a substitute to standard parametric approaches, or in situations where parametric methods do not exist. In this tutorial, we cover R code to implement the percentile bootstrap in a few situations: one-sample estimation and the comparison of two independent groups for measures of central tendency (means and trimmed means) and spread. For each example, we explain how to derive a bootstrap distribution, and how to get a confidence interval and a p value from that distribution. We also demonstrate how to run a simulation to assess the behaviour of the bootstrap. In some situations, the bootstrap performs poorly, such as when making inferences about the mean. But for other purposes, it is the only known method that works well over a broad range of situations, such as when comparing medians and there are tied (duplicated) values. More broadly, combining the percentile bootstrap with robust estimators, i.e. estimators that are not overly sensitive to outliers, the bootstrap can help users gain a deeper understanding of their data, relative to conventional methods.