Literature DB >> 21631265

The College of American Pathologists and National Society for Histotechnology workload study.

Shane K Kohl1, Sue E Lewis, Janet Tunnicliffe, Robert L Lott, Lena T Spencer, Freida L Carson, Rhona J Souers, Robert H Knapp, Saeid Movahedi-Lankarani, Thomas S Haas, Richard W Brown.   

Abstract

Limited data exist in regard to productivity and staffing in the anatomic pathology laboratory. In 2004, the National Society for Histotechnology (NSH) conducted a pilot study to examine productivity and staffing in the histology laboratory. After review of the data, The College of American Pathologists (CAP)/NSH Histotechnology Committee concluded that a larger survey was required to further address and expand on the pilot study findings. In 2007, a total of 2674 surveys were sent out to North American laboratories. From the responses, comparisons of laboratory demographics and productivity were examined by institution type and workload volume. Productivity was measured as the number of paraffin-embedded tissue blocks processed per full-time equivalent per year. This manuscript presents and discusses the data collected from the CAP/NSH Workload Study.

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Year:  2011        PMID: 21631265     DOI: 10.5858/2010-0288-CP.1

Source DB:  PubMed          Journal:  Arch Pathol Lab Med        ISSN: 0003-9985            Impact factor:   5.534


  4 in total

1.  National survey of anatomical pathology centres in Italy: the questionnaire.

Authors:  G Mazzoleni; M Barbareschi; M Basciu; D Fassinato; P Vian; F Vittadello; M Truini; G De Rosa; S M Mezzopera
Journal:  Pathologica       Date:  2019-03

2.  X-ray microtomosynthesis of unstained pathology tissue samples.

Authors:  David T Nguyen; Thomas C Larsen; Muyang Wang; Russel H Knutsen; Zhihong Yang; Eric E Bennett; Dumitru Mazilu; Zu-Xi Yu; Xi Tao; Danielle R Donahue; Ahmed M Gharib; Christopher K E Bleck; Joel Moss; Alan T Remaley; Beth A Kozel; Han Wen
Journal:  J Microsc       Date:  2021-02-18       Impact factor: 1.758

3.  Histopathology: ditch the slides, because digital and 3D are on show.

Authors:  Ilaria Jansen; Marit Lucas; C Dilara Savci-Heijink; Sybren L Meijer; Henk A Marquering; Daniel M de Bruin; Patricia J Zondervan
Journal:  World J Urol       Date:  2018-02-02       Impact factor: 4.226

4.  Deep learning classification of lung cancer histology using CT images.

Authors:  Tafadzwa L Chaunzwa; Ahmed Hosny; Yiwen Xu; Andrea Shafer; Nancy Diao; Michael Lanuti; David C Christiani; Raymond H Mak; Hugo J W L Aerts
Journal:  Sci Rep       Date:  2021-03-09       Impact factor: 4.379

  4 in total

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