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Self-Reported Recovery from 2-Week 12-Hour Shift Work Schedules: A 14-Day Follow-Up
Suzanne L. Merkus,Kari Anne Holte,Maaike A. Huysmans,Peter M. van de Ven,Willem van Mechelen,Allard J. van der Beek 한국산업안전공단 산업안전보건연구원 2015 Safety and health at work Vol.6 No.3
Background: Recovery from fatigue is important in maintaining night workers’ health. This study compared the course of self-reported recovery after 2-week 12-hour schedules consisting of either night shifts or swing shifts (i.e., 7 night shifts followed by 7 day shifts) to such schedules consisting of only day work. Methods: Sixty-one male offshore employeesd20 night workers, 16 swing shift workers, and 25 day workersdrated six questions on fatigue (sleep quality, feeling rested, physical and mental fatigue, and energy levels; scale 1e11) for 14 days after an offshore tour. After the two night-work schedules, differences on the 1st day (main effects) and differences during the follow-up (interaction effects) were compared to day work with generalized estimating equations analysis. Results: After adjustment for confounders, significant main effects were found for sleep quality for night workers (1.41, 95% confidence interval 1.05e1.89) and swing shift workers (1.42, 95% confidence interval 1.03e1.94) when compared to day workers; their interaction terms were not statistically significant. For the remaining fatigue outcomes, no statistically significant main or interaction effects were found. Conclusion: After 2-week 12-hour night and swing shifts, only the course for sleep quality differed from that of day work. Sleep quality was poorer for night and swing shift workers on the 1st day off and remained poorer for the 14-day follow-up. This showed that while working at night had no effect on feeling rested, tiredness, and energy levels, it had a relatively long-lasting effect on sleep quality.
Self-Reported Recovery from 2-Week 12-Hour Shift Work Schedules: A 14-Day Follow-Up
Merkus, Suzanne L.,Holte, Kari Anne,Huysmans, Maaike A.,van de Ven, Peter M.,van Mechelen, Willem,van der Beek, Allard J. Occupational Safety and Health Research Institute 2015 Safety and health at work Vol.6 No.3
Background: Recovery from fatigue is important in maintaining night workers' health. This study compared the course of self-reported recovery after 2-week 12-hour schedules consisting of either night shifts or swing shifts (i.e., 7 night shifts followed by 7 day shifts) to such schedules consisting of only day work. Methods: Sixty-one male offshore employees-20 night workers, 16 swing shift workers, and 25 day workers-rated six questions on fatigue (sleep quality, feeling rested, physical and mental fatigue, and energy levels; scale 1-11) for 14 days after an offshore tour. After the two night-work schedules, differences on the $1^{st}$ day (main effects) and differences during the follow-up (interaction effects) were compared to day work with generalized estimating equations analysis. Results: After adjustment for confounders, significant main effects were found for sleep quality for night workers (1.41, 95% confidence interval 1.05-1.89) and swing shift workers (1.42, 95% confidence interval 1.03-1.94) when compared to day workers; their interaction terms were not statistically significant. For the remaining fatigue outcomes, no statistically significant main or interaction effects were found. Conclusion: After 2-week 12-hour night and swing shifts, only the course for sleep quality differed from that of day work. Sleep quality was poorer for night and swing shift workers on the $1^{st}$ day off and remained poorer for the 14-day follow-up. This showed that while working at night had no effect on feeling rested, tiredness, and energy levels, it had a relatively long-lasting effect on sleep quality.
Kim, Jin Hwan,van Beek JR, Edwin,Murchison, John T,Marin, Aleksander,Mirsadraee, Saeed The Korean Academy of Tuberculosis and Respiratory 2015 Tuberculosis and Respiratory Diseases Vol.78 No.3
Accurate lymph node staging of lung cancer is crucial in determining optimal treatment plans and predicting patient outcome. Currently used lymph node maps have been reconciled to the internationally accepted International Association for the Study of Lung Cancer (IASLC) map published in the seventh edition of TNM classification system of malignant tumours. This article provides computed tomographic illustrations of the IASLC nodal map, to facilitate its application in day-to-day clinical practice in order to increase the appropriate classification in lung cancer staging.
( Jin Hwan Kim ),( Edwin Jr Van Beek ),( John T Murchison ),( Aleksander Marin ),( Saeed Mirsadraee ) 대한결핵 및 호흡기학회 2015 Tuberculosis and Respiratory Diseases Vol.78 No.3
Accurate lymph node staging of lung cancer is crucial in determining optimal treatment plans and predicting patient outcome. Currently used lymph node maps have been reconciled to the internationally accepted International Association for the Study of Lung Cancer (IASLC) map published in the seventh edition of TNM classification system of malignant tumours. This article provides computed tomographic illustrations of the IASLC nodal map, to facilitate its application in day-to-day clinical practice in order to increase the appropriate classification in lung cancer staging.
Lee, Geewon,Lee, Ho Yun,Park, Hyunjin,Schiebler, Mark L.,van Beek, Edwin J.R.,Ohno, Yoshiharu,Seo, Joon Beom,Leung, Ann Elsevier 2017 European journal of radiology Vol.86 No.-
<P><B>Abstract</B></P> <P>With the development of functional imaging modalities we now have the ability to study the microenvironment of lung cancer and its genomic instability. <I>Radiomics</I> is defined as the use of automated or semi-automated post-processing and analysis of large amounts of quantitative imaging features that can be derived from medical images. The automated generation of these analytical features helps to quantify a number of variables in the imaging assessment of lung malignancy. These imaging features include: tumor spatial complexity, elucidation of the tumor genomic heterogeneity and composition, subregional identification in terms of tumor viability or aggressiveness, and response to chemotherapy and/or radiation. Therefore, a radiomic approach can help to reveal unique information about tumor behavior. Currently available radiomic features can be divided into four major classes: (a) morphological, (b) statistical, (c) regional, and (d) model-based. Each category yields quantitative parameters that reflect specific aspects of a tumor. The major challenge is to integrate radiomic data with clinical, pathological, and genomic information to decode the different types of tissue biology. There are many currently available radiomic studies on lung cancer for which there is a need to summarize the current state of the art.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Radiomics is the post-processing and analysis of large amounts of quantitative imaging features that can be derived from medical images. </LI> <LI> Radiomics features can reflect the spatial complexity, genomic heterogeneity, and subregional identification of lung cancer. </LI> <LI> Currently available radiomic features can be divided into four major categories. </LI> <LI> The major challenge is to integrate radiomic data with clinical, pathological, and genomic information. </LI> </UL> </P>