BACKGROUND: Worldwide there is an alarming increase in the caesarean delivery rate which has become a controversial topic. However, the reasons for this tendency are not clear. For example, in Greece alone, rates increased by almost 50% from 1983 to 1996. In order to better understand the causes of this phenomenon, we need to examine closely what groups of women undergo caesarean section (CS). To achieve this, it is essential to use a system that will enable us to monitor and compare caesarean delivery rates. Such a classification system should be easily adopted by obstetricians, midwives, and public health services.
METHODS: A review search of electronic databases concerning medical care was held from December 2020 to January 2021 in order to find systematic reviews which describe either theoretical or practical CS classification systems.
RESULTS: The most common classification systems fall into three main categories based on indication, urgency and maternal-based characteristics. According to users the highest rated classification system was women-based classifications in general. In particular the Robson Ten Group Classification System was considered to be the most valid to meet current local and international standards. The Robson classification system is praised for its robustness, simplicity, flexibility, and reproducibility.
CONCLUSIONS: The right implementation of the Robson Ten Group Classification System can facilitate an in-depth analysis of the main groups that increase CS rates and can be used to both review and monitor delivery practices both in Greece and abroad.

Recent conceptualizations of interoception suggest several facets to this construct, including \"interoceptive sensibility\" and \"self-report interoceptive scales\", both of which are assessed with questionnaires. Although these conceptual efforts have helped move the field forward, uncertainty remains regarding whether current measures converge on their measurement of a common construct. To address this question, we first identified -via a systematic review- the most cited questionnaires of interoceptive sensibility. Then, we examined their correlations, their overall factorial structure, and their network structure in a large community sample (n = 1003). The results indicate that these questionnaires tap onto distinct constructs, with low overall convergence and interrelationships between questionnaire items. This observation mitigates the interpretation and replicability of findings in self-report interoception research. We call for a better match between constructs and measures.

The factor structure of the Insomnia Severity Index (ISI) is discussed, with examples being drawn from about 13 disparate reported models. A systematic appraisal of procedural details of studies investigating the dimensionality of the ISI was performed. The databases CINAHL, Cochrane Library, Embase, Medline, and PsycINFO were searched thoroughly, and all articles in English, published, and/or available online as of 14th March 2019, were included. Meta-analyses of Cronbach\'s alpha and structural validity measures were performed. Several inadequacies or omissions were found in analyses using the three groups of measures. Fifteen out of 20 studies did not employ either EFA or CFA, 10 studies did not report CFA, while five did not report EFA. The most common omissions related to multivariate normality, final model selection based on consideration of fit indices, parsimony, and failure to consider the theoretical construct in the final model selection. Meta-analysis showed that CFA outcome is more robust compared to EFA, two-factor solution is the vigorous presentation of dimensionality compared to a three-factor solution, and ISI has a high pooled Cronbach\'s alpha. Further validation of the disparate models of the ISI is needed with more comprehensive reporting guidelines, such as the one suggested in the present review.

Since the accelerating development of technology applied to team sports and its subsequent high amount of information available, the need for data mining leads to the use of data reduction techniques such as Principal Component Analysis (PCA). This systematic review aims to identify determinant variables in soccer, basketball and rugby using exploratory factor analysis for, training design, performance analysis and talent identification. Three electronic databases (PubMed, Web of Science, SPORTDiscus) were systematically searched and 34 studies were finally included in the qualitative synthesis. Through PCA, data sets were reduced by 75.07%, and 3.9 ± 2.53 factors were retained that explained 80 ± 0.14% of the total variance. All team sports should be analyzed or trained based on the high level of aerobic capacity combined with adequate levels of power and strength to perform repeated high-intensity actions in a very short time, which differ between team sports. Accelerations and decelerations are mainly significant in soccer, jumps and landings are crucial in basketball, and impacts are primarily identified in rugby. Besides, from these team sports, primary information about different technical/tactical variables was extracted such as (a) soccer: occupied space, ball controls, passes, and shots; (b) basketball: throws, rebounds, and turnovers; or (c) rugby: possession game pace and team formation. Regarding talent identification, both anthropometrics and some physical capacity measures are relevant in soccer and basketball. Although overall, since these variables have been identified in different investigations, further studies should perform PCA on data sets that involve variables from different dimensions (technical, tactical, conditional).

This systematic review aimed to synthesise multimorbidity profiling literature to identify replicable and clinically meaningful groupings of multimorbidity. We searched six electronic databases (Medline, EMBASE, PsycINFO, CINAHL, Scopus, and Web of Science) for articles reporting multimorbidity profiles. The identified profiles were synthesised with multidimensional scaling, stratified by type of statistical analysis used in the derivation of profiles. The 51 studies that met inclusion criteria reported results of 98 separate analyses of multimorbidity profiling, with a total of 407 multimorbidity profiles identified. The statistical techniques used to identify multimorbidity profiles were exploratory factor analysis, cluster analysis of diseases, cluster analysis of people, and latent class analysis. Reporting of methodological details of statistical methods was often incomplete. The discernible groupings of multimorbidity took the form of both discrete categories and continuous dimensions. Mental health conditions and cardio-metabolic conditions grouped along identifiable continua in the synthesised results of all four methods. Discrete groupings of chronic obstructive pulmonary disease with asthma, falls and fractures with sensory deficits and of Parkinson\'s disease and cognitive decline where partially replicable (identifiable in the results of more than one method), while clustering of musculoskeletal conditions and clustering of reproductive systems were each observed only in one statistical approach. The two most replicable multimorbidity profiles were mental health conditions and cardio-metabolic conditions. Further studies are needed to understand aetiology and evolution of these multimorbidity groupings. Guidelines for strengthening the reporting of multimorbidity profiling studies are proposed.

BACKGROUND: The Pittsburgh Sleep Quality Index (PSQI) dimensionality is much debated, with the greatest number of reported factor structures. Therefore, this review appraised the methodologies of studies investigating the factor structure of the PSQI.
METHODS: MEDLINE, PsycInfo, AJOL, BASE, Cochrane Library, Directory of Open Access Journals (Lund University), CINAHL, and Embase were searched systematically to include articles published till 23rd March, 2018. The articles with the objective of factor analysis of the PSQI (20 articles) or with a major section on the same subject (25 articles) were included. There was no limitation about participant characteristics. Descriptive analysis of articles for measures of the suitability of the data for factor analysis, details of the exploratory factor analysis (EFA) and details of the confirmatory factor analysis (CFA) was performed.
RESULTS: The analysis used by the majority did not employ the simplest scheme for interpreting the observed data: the parsimony principle. Other shortcomings included under- or non-reporting of sample adequacy measures (11 out of 45 articles), non-use of EFA (20 out of 45 articles), use of EFA without relevant details, non-use of CFA (11 out of 45 articles), and use of CFA without relevant details. Overall, 31 out of 45 articles did not use either EFA or CFA.
CONCLUSIONS: We conclude that the various PSQI factor structures for standard sleep assessment in research and clinical settings may need further validation.
BACKGROUND: Not applicable because this was a review of existing literature.