Background: Qualitative self- or parent-reports used in assessing children's behavioral disorders are often inconvenient to collect and can be misleading due to missing information, rater biases, and limited validity. A data-driven approach to quantify behavioral disorders could alleviate these concerns. This study proposes a machine learning approach to identify screams in voice recordings that avoids the need to gather large amounts of clinical data for model training.
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April 2018
Background: Klinefelter's syndrome is a sex chromosome abnormality affecting approximately 1 in 1000 men. There have been suggestions that it is associated with a higher than average prevalence of sexual offending but to what extent does research evidence support this assertion?
Aims: This study aimed to conduct a systematic review of published research to establish the prevalence of sexual offending in men with Klinefelter's syndrome.
Method: The databases MEDLINE, PsycINFO and EMBASE were searched from inception until 31 December 2016 by using a range of terms for Klinefelter's syndrome and for sexual offending.
Aim: This study assessed neonatal outcomes following implementation of a structured, practical approach for promotion of breastmilk expression in mothers of very preterm infants.
Methods: A multidisciplinary team developed a protocol that focussed on the enhancement of expression of human milk by mothers of very preterm infants. A pre- and poststudy intervention was subsequently performed.
The letters from a Community Mental Health Team to patients' general practitioners were reviewed to identify the standard of informed consent documentation. A tool was developed to achieve the expected standards for informed consent documentation and its effects on practice evaluated. Statistically significant improvements were shown using our tool in documentation of discussion of risks; increased by 36% (P ≤ 0.
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