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The Clinical Biochemist. Reviews|June 18, 2019
Indirect Reference Intervals: Harnessing the Power of Stored Laboratory DataChristopher-John L Farrell, Lan NguyenClinical Endocrinology|September 27, 2017
Parathyroid hormone: Data mining for age-related reference intervals in adultsChristopher-John L Farrell, Lan Nguyen, Andrew C CarterClinical Chemistry and Laboratory Medicine|October 30, 2021
Decision support or autonomous artificial intelligence? The case of wrong blood in tube errorsChristopher-John L FarrellInternational 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 GiannoutsosAnnals of Clinical Biochemistry|May 6, 2016
Serum indices: managing assay interferenceChristopher-John L Farrell, Andrew C CarterClinical 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 HerrmannClinical Chemistry and Laboratory Medicine|June 3, 2014
Impact of assay design on test performance: lessons learned from 25-hydroxyvitamin DChristopher-John L Farrell, Joshua Soldo, Brett McWhinney, et al.Clinical Chemistry and Laboratory Medicine|July 1, 2016
Assessment of vitamin D status - a changing landscapeMarkus Herrmann, Christopher-John L Farrell, Irene Pusceddu, et al.Clinical Chemistry|January 11, 2012
State-of-the-art vitamin D assays: a comparison of automated immunoassays with liquid chromatography-tandem mass spectrometry methodsChristopher-John L Farrell, Steven Martin, Brett McWhinney, et al.The Journal of Physical Chemistry. A|October 12, 2021
Improving Perturbation Theory for Open-Shell Molecules via Self-ConsistencyLan Nguyen TranPageof 56