Examining the normality assumption of a design-comparable effect size in single-case designs.

What Works Clearinghouse (WWC, 2022) recommends a design-comparable effect size (D-CES; i.e., gAB) to gauge an intervention in single-case experimental design (SCED) studies, or to synthesize findings in meta-analysis. So far, no research has examined gAB's performance under non-normal distributions. This study expanded Pustejovsky et al. (2014) to investigate the impact of data distributions, number of cases (m), number of measurements (N), within-case reliability or intra-class correlation (ρ), ratio of variance components (λ), and autocorrelation (ϕ) on gAB in multiple-baseline (MB) design. The performance of gAB was assessed by relative bias (RB), relative bias of variance (RBV), MSE, and coverage rate of 95% CIs (CR). Findings revealed that gAB was unbiased even under non-normal distributions. gAB's variance was generally overestimated, and its 95% CI was over-covered, especially when distributions were normal or nearly normal combined with small m and N. Large imprecision of gAB occurred when m was small and ρ was large. According to the ANOVA results, data distributions contributed to approximately 49% of variance in RB and 25% of variance in both RBV and CR. m and ρ each contributed to 34% of variance in MSE. We recommend gAB for MB studies and meta-analysis with N ≥ 16 and when either (1) data distributions are normal or nearly normal, m = 6, and ρ = 0.6 or 0.8, or (2) data distributions are mildly or moderately non-normal, m ≥ 4, and ρ = 0.2, 0.4, or 0.6. The paper concludes with a discussion of gAB's applicability and design-comparability, and sound reporting practices of ES indices.

A Retrospective Study of Unicompartmental Knee Arthroplasty Functional Outcome and the Incidence of Medial Meniscus Posterior Root Tear in Spontaneous Osteonecrosis of the Knee.

BACKGROUND: Spontaneous osteonecrosis of the knee (SONK) can lead to severe knee osteoarthritis predominantly localized to the medial compartment. We aimed to determine whether unicompartmental knee arthroplasty was an effective treatment for primary SONK. METHODS: We analyzed the functional outcomes in 23 patients with SONK (with a magnetic resonance imaging- (MRI-) confirmed diagnosis) who underwent UKA at a single center. The mean follow-up time was 67 months post-UKA. RESULTS: Significant improvements in function were indicated by reduced Oxford Knee and Visual Analogue Scale scores after UKA, and there were no specific complications after the procedures. The incidence of MRI-identified medial meniscus posterior root tear (MMPRT) was 69.6% (16/23). CONCLUSION: Unicompartmental arthroplasty for SONK is less destructive to the native knee structure than total knee arthroplasty but can achieve comparable prognosis with strict patient selection. While the precise etiology of SONK is unknown, one theory posits that a MMPRT may change the biomechanical circumstances of the knee joint, leading to osteonecrosis. Although not confirmatory, the high prevalence rate suggests that MMPRT may have a key role in the development of SONK. UKA is an effective treatment option for SONK, resulting in significant functional improvement. Long-term (>10 years) outcomes should be investigated.

Dealing with missing data by EM in single-case studies.

Single-case experimental design (SCED) research plays an important role in establishing and confirming evidence-based practices. Due to multiple measures of a target behavior in such studies, missing information is common in their data. The expectation-maximization (EM) algorithm has been successfully applied to deal with missing data in between-subjects designs, but only in a handful of SCED studies. The present study extends the findings from Smith, Borckardt, and Nash (2012) and Velicer and Colby (2005b, Study 2) by systematically examining the performance of EM in a baseline-intervention (or AB) design under various missing rates, autocorrelations, intervention phase lengths, and magnitudes of effects, as well as two fitted models. Three indicators of an intervention effect (baseline slope, level shift, and slope change) were estimated. The estimates' relative bias, root-mean squared error, and relative bias of the estimated standard error were used to assess EM's performance. The findings revealed that autocorrelation impacted the estimates' qualities most profoundly. Autocorrelation interacted with missing rate in impacting the relative bias of the estimates, impacted the root-mean squared error nonlinearly, and interacted with the fitted model in impacting the relative bias of the estimated standard errors. A simpler model without autocorrelation can be used to estimate baseline slope and slope change in time-series data. EM is recommended as a principled method to handle missing data in SCED studies. Two decision trees are presented to assist researchers and practitioners in applying EM. Emerging research directions are identified for treating missing data in SCED studies.

In vitro neuropeptide Y mRNA expressing model for screening essences that may affect appetite using Rolf B1.T cells.

Neuropeptide Y (NPY) is the most important appetite regulator. This study aimed to establish an in vitro NPY mRNA expression model for screening essences to determine if they are an appetite stimulator or inhibitor. We cultured the olfactory nerve cells Rolf B1.T for 2 days and then treated the cells with the known appetite inhibitor limonene and stimulator linalool. It was found that linalool could significantly stimulate NPY mRNA expression in 10 min, and limonene had the opposite effect. Similar results were also found in primary olfactory ensheathing cells isolated from rats. Further clinical trials using human subjects found that, when 10 min of treatment was applied, linalool indeed increased the serum NPY level in human peripheral blood. Limonene, on the other hand, decreased the serum NPY level. Thus, NPY mRNA expression in Rolf B1.T cells could be used as an in vitro model for screening essences that may affect appetite.