Platelet-rich plasma failed to demonstrate any statistically significant improvement in HMI or hair count in women with congenital female pattern hair loss. The patient survey results suggest a therapeutic advantage of PRP as perceived by patients but not according to hair count or HMI.

Several investigations have considered the spelling abilities of children with reading disability; however, the spelling patterns of children with a language learning disability (LLD) have been largely ignored. This study examined the spelling error patterns of three groups of children who met strict inclusion criteria-those with a known LLD (n = 8), chronological-age-matched peers (CA; n = 8), and a younger spelling-age-matched group (SA; n = 8). An experimental spelling measure was specially designed and administered to elucidate the underlying linguistic features (clusters, digraphs, etc.) and linguistic classifications (phonological, orthographical, morphological) of misspellings. Based on inferential statistical analyses, a general pattern was that the LLD group and the SA group always differed from the CA group, whereas the LLD group performed similarly to the SA group. This finding lends credence to the hypothesis that children with an LLD, like children with reading disability, are delayed in spelling development rather than following a deviant developmental process. However, a qualitative analysis indicated two specific patterns. First, the LLD group had more trouble than did the SA group in representing the basic phonological structure of words, when complexity was increased by word length or linguistic structure. Second, in contrast to the SA group, the LLD group had greater omissions of inflected and derived morphological markers. These findings point to the critical role of morphology as the mediator between and form and meaning.

No abstract

In a longitudinal observational study of 94 patients (39M:55F, mean age 69) at elevated risk for developing post herpetic neuralgia (PHN), the natural history of pain during the first 6 months after herpes zoster (HZ) rash onset was determined. Pain severity and impact were rated using pain-VAS, SF-MPQ, and MPI.Applying a definition of PHN of average daily pain >0/100 on the pain VAS during the last 48 hours, 30 subjects had PHN at 6 months. These 30 subjects reported more pain and a higher SF-MPQ score (p<0.01) at study inclusion than the 64 subjects whose pain completely resolved by 6 months. At 6 months, mean daily pain in the PHN group was 11/100 (95% CI 5,16) and only nine of these subjects were still taking prescription medication for HZ pain. The rate of recovery (pain severity over time) was the same in the PHN and no-pain groups. At study inclusion, the SF-MPQ and MPI scores in our PHN group were similar to historical controls with chronic severe PHN enrolled in clinical trials, but by 6 months the scores in our PHN subjects were significantly lower than historic controls. Only two subjects met the more stringent criteria for 'clinically meaningful' PHN at 6 months (≥30/100 on the pain VAS).Defining PHN as average daily pain >0/100 at 6 months after rash onset appears to substantially overestimate the number of HZ patients negatively impacted by ongoing pain and disability.

Across the nation, quality leadership and positive school climate are critical to the success of every principal, student, and school. As a result, this study examined the relationship between teacher perceptions of the degree to which a principal displays the factors of transformational leadership and the perceived school climate. A purposeful sample of 399 teachers, representing five high schools in a large urban school district in southeast Texas, were administered the Multifactor Leadership Questionnaire to assess his or her principal in terms of being a transformational leader and the Organizational Climate Description Questionnaire for Secondary Schools to evaluate the climate of his or her respective school. The results of the HLM analysis indicated that there was a correlation between transformational leadership and the supportive, engaged, and frustrated elements of a school’s climate.

Correlational analyses are one of the most popular quantitative methods, yet also one of the mostly frequently misused methods in social and behavioral research, especially when analyzing ordinal data from Likert or other rating scales. Although several correlational analysis options have been developed for ordinal data, there seems to be a lack of didactically written literature illustrating the appropriate use and differences among them. The purpose of this paper is to provide a synthesis of correlational analysis options when analyzing ordinal data. These options span from the traditional methods, such as Pearson's r, to more recent developments, such as Bayesian estimation of polychoric correlations. An illustration of these methods utilizing a contemporary dataset is provided.

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The rising popularity of explainable artificial intelligence (XAI) to understand high-performing black boxes, also raised the question of how to evaluate explanations of machine learning (ML) models. While interpretability and explainability are often presented as a subjectively validated binary property, we consider it a multi-faceted concept. We identify 12 conceptual properties, such as Compactness and Correctness, that should be evaluated for comprehensively assessing the quality of an explanation. Our so-called Co-12 properties serve as categorization scheme for systematically reviewing the evaluation practice of more than 300 papers published in the last 7 years at major AI and ML conferences that introduce an XAI method. We find that 1 in 3 papers evaluate exclusively with anecdotal evidence, and 1 in 5 papers evaluate with users. We also contribute to the call for objective, quantifiable evaluation methods by presenting an extensive overview of quantitative XAI evaluation methods. This systematic collection of evaluation methods provides researchers and practitioners with concrete tools to thoroughly validate, benchmark and compare new and existing XAI methods. This also opens up opportunities to include quantitative metrics as optimization criteria during model training in order to optimize for accuracy and interpretability simultaneously.