5 research outputs found

    Manifestation and parental assessment of children’s cancer pain at home: an exploratory mixed methods study

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    Aims and objectives To describe pain manifestation in children with cancer at home and understand how parents assess this pain. Background Pain is experienced by children with cancer throughout their cancer journey. Short‐term, and into survivorship, pain has negative physical and psychological consequences. Changes in treatment location mean children with cancer spend more time at home. Little is known about pain experienced by children at home or how parents assess this pain. Design A mixed methods convergent parallel study was reported using STROBE. Method Parents of children with cancer on active treatment were recruited from one tertiary cancer centre. Parental attitudes towards pain expression were assessed using surveys. Parents recorded their child’s pain manifestation in pain diaries kept for one month. Interviews captured a deeper understanding of pain manifestation and how parents assess this pain at home. Integration occurred after each data collection method was analysed separately. Results Predominantly children were not in pain at home. However, most children experienced at least one episode of problematic pain over the pain diary period. Surveys showed parents held misconceptions regarding children’s pain expression. Interviews diverge from surveys and suggest parents used a range of information sources to assess pain. Conclusion Children with cancer may differ from one another in the manifestation of pain at home resulting in multiple pain trajectories. Parents of children with cancer are able to adequately assess their child’s pain using information from multiple source

    Additional file 3 of PredictCBC-2.0: a contralateral breast cancer risk prediction model developed and validated in ~ 200,000 patients

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    Additional file 1: Table S3. Patient and primary breast cancer characteristics per study

    Additional file 2 of PredictCBC-2.0: a contralateral breast cancer risk prediction model developed and validated in ~ 200,000 patients

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    Additional file 2: Table S1. Description of the studies included in the analyses
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