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Clinical Chemistry and Laboratory Medicine
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December 2, 2024
Influence of data visualisations on laboratorians' acceptance of method comparison studies
Christopher-John L Farrell
Clinical Chemistry and Laboratory Medicine
|
October 30, 2021
Decision support or autonomous artificial intelligence? The case of wrong blood in tube errors
Christopher-John L Farrell
The Medical Journal of Australia
|
August 8, 2013
Vitamin B12 and folate tests: interpret with care
Christopher-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 results
Christopher-John L Farrell, John Giannoutsos
The Clinical Biochemist. Reviews
|
June 18, 2019
Indirect Reference Intervals: Harnessing the Power of Stored Laboratory Data
Christopher-John L Farrell, Lan Nguyen
Annals of Clinical Biochemistry
|
May 6, 2016
Serum indices: managing assay interference
Christopher-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 adulthood
Christopher-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 adults
Christopher-John L Farrell, Lan Nguyen, Andrew C Carter
Page
of 2
Search research articles
Search
Showing results (1-10 of 17) with videos related to
Sort By:
Page
of 2
Clinical Chemistry and Laboratory Medicine
|
December 2, 2024
Influence of data visualisations on laboratorians' acceptance of method comparison studies
Christopher-John L Farrell
Clinical Chemistry and Laboratory Medicine
|
October 30, 2021
Decision support or autonomous artificial intelligence? The case of wrong blood in tube errors
Christopher-John L Farrell
The Medical Journal of Australia
|
August 8, 2013
Vitamin B12 and folate tests: interpret with care
Christopher-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 results
Christopher-John L Farrell, John Giannoutsos
The Clinical Biochemist. Reviews
|
June 18, 2019
Indirect Reference Intervals: Harnessing the Power of Stored Laboratory Data
Christopher-John L Farrell, Lan Nguyen
Annals of Clinical Biochemistry
|
May 6, 2016
Serum indices: managing assay interference
Christopher-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 adulthood
Christopher-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 adults
Christopher-John L Farrell, Lan Nguyen, Andrew C Carter
Page
of 2