Cervical cytology reading times: A comparison between ThinPrep Imager and conventional methods

Elizabeth Davey*, Les Irwig, Petra Macaskill, Siew F. Chan, Jefferson D'Assuncao, Adele Richards, Annabelle Farnsworth

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)

Abstract

We aimed to compare the times cytologists spend reviewing cervical cytology slides processed by the ThinPrep Imager® (TPI) with times they spend examining conventional cytology (CC) slides. We also aimed to examine the effect of cytologists' experience on reading times. Using a cross-sectional analytical design, we analyzed routine laboratory data, collected retrospectively over 7 months, for 41 cytologists, including paired data for 20 who read both TPI and CC slides. For the 20 cytologists who read both types of cytology, the mean reading rate was 13.3 slides per hour for TPI slides and 6.1 slides per hour for CC slides. The mean within-reader difference between TPI and CC rates was 7.2 slides per hour (P < 0.001). For CC reading, mean times did not differ between those who were additionally trained to read TPI slides and those who only read CC. Slower readers had greater increases in speed when using the TPI compared with CC reading than did faster readers (P < 0.001). More experienced cytologists tended to read CC slides more quickly than did those less experienced, but experience did not affect TPI reading times or within-reader differences in reading times between cytology types. The TPI significantly reduced reading times compared with CC. This reduction was greater amongst slower readers, and was unrelated to experience.

Original languageEnglish
Pages (from-to)550-554
Number of pages5
JournalDiagnostic Cytopathology
Volume35
Issue number9
DOIs
Publication statusPublished - Sept 2007
Externally publishedYes

Keywords

  • Cervical cytology
  • Reading times
  • ThinPrep Imager

Fingerprint

Dive into the research topics of 'Cervical cytology reading times: A comparison between ThinPrep Imager and conventional methods'. Together they form a unique fingerprint.

Cite this