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Christopher-John L Farrell

Showing results (1-10 of 17) with videos related to

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Clinical Chemistry and Laboratory Medicine|December 2, 2024
Influence of data visualisations on laboratorians' acceptance of method comparison studiesChristopher-John L Farrell
Clinical Chemistry and Laboratory Medicine|October 30, 2021
Decision support or autonomous artificial intelligence? The case of wrong blood in tube errorsChristopher-John L Farrell
The Medical Journal of Australia|August 8, 2013
Vitamin B12 and folate tests: interpret with careChristopher-John L Farrell
International Journal of Laboratory Hematology|March 11, 2022
Machine learning models outperform manual result review for the identification of wrong blood in tube errors in complete blood count resultsChristopher-John L Farrell, John Giannoutsos
The Clinical Biochemist. Reviews|June 18, 2019
Indirect Reference Intervals: Harnessing the Power of Stored Laboratory DataChristopher-John L Farrell, Lan Nguyen
Annals of Clinical Biochemistry|May 6, 2016
Serum indices: managing assay interferenceChristopher-John L Farrell, Andrew C Carter
Clinical Chemistry|July 15, 2016
Hemolysis Interference: Are Laboratories Getting the Information They Need?Christopher-John L Farrell, Andrew C Carter
Clinical Chemistry and Laboratory Medicine|February 17, 2017
Data mining for age-related TSH reference intervals in adulthoodChristopher-John L Farrell, Lan Nguyen, Andrew C Carter
Clinical Chemistry and Laboratory Medicine|March 2, 2013
Red cell or serum folate: what to do in clinical practice?Christopher-John L Farrell, Susanne H Kirsch, Markus Herrmann
Clinical Endocrinology|September 27, 2017
Parathyroid hormone: Data mining for age-related reference intervals in adultsChristopher-John L Farrell, Lan Nguyen, Andrew C Carter
Pageof 2

Showing results (1-10 of 17) with videos related to

Sort By:
Pageof 2
Clinical Chemistry and Laboratory Medicine|December 2, 2024
Influence of data visualisations on laboratorians' acceptance of method comparison studiesChristopher-John L Farrell
Clinical Chemistry and Laboratory Medicine|October 30, 2021
Decision support or autonomous artificial intelligence? The case of wrong blood in tube errorsChristopher-John L Farrell
The Medical Journal of Australia|August 8, 2013
Vitamin B12 and folate tests: interpret with careChristopher-John L Farrell
International Journal of Laboratory Hematology|March 11, 2022
Machine learning models outperform manual result review for the identification of wrong blood in tube errors in complete blood count resultsChristopher-John L Farrell, John Giannoutsos
The Clinical Biochemist. Reviews|June 18, 2019
Indirect Reference Intervals: Harnessing the Power of Stored Laboratory DataChristopher-John L Farrell, Lan Nguyen
Annals of Clinical Biochemistry|May 6, 2016
Serum indices: managing assay interferenceChristopher-John L Farrell, Andrew C Carter
Clinical Chemistry|July 15, 2016
Hemolysis Interference: Are Laboratories Getting the Information They Need?Christopher-John L Farrell, Andrew C Carter
Clinical Chemistry and Laboratory Medicine|February 17, 2017
Data mining for age-related TSH reference intervals in adulthoodChristopher-John L Farrell, Lan Nguyen, Andrew C Carter
Clinical Chemistry and Laboratory Medicine|March 2, 2013
Red cell or serum folate: what to do in clinical practice?Christopher-John L Farrell, Susanne H Kirsch, Markus Herrmann
Clinical Endocrinology|September 27, 2017
Parathyroid hormone: Data mining for age-related reference intervals in adultsChristopher-John L Farrell, Lan Nguyen, Andrew C Carter
Pageof 2