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Record W2112621233 · doi:10.1111/ajt.12462

Comprehensive Assessment and Standardization of Solid Phase Multiplex-Bead Arrays for the Detection of Antibodies to HLA-Drilling Down on Key Sources of Variation

2013· letter· en· W2112621233 on OpenAlexaff
Elaine F. Reed, P. Nagesh Rao, Zuo‐Feng Zhang, Howard M. Gebel, Robert A. Bray, Indira Guleria, John G. Lunz, Thalachallour Mohanakumar, Peter Nickerson, Anat R. Tambur, Adriana Zeevi, Peter S. Heeger, David Gjertson

Bibliographic record

VenueAmerican Journal of Transplantation · 2013
Typeletter
Languageen
FieldMedicine
TopicRenal Transplantation Outcomes and Treatments
Canadian institutionsUniversity of ManitobaShared Health
FundersNational Institute of Allergy and Infectious DiseasesU.S. Public Health Service
KeywordsMultiplexStandardizationMedicineProtocol (science)TransplantationSerial dilutionCoefficient of variationStatisticsComputer scienceBioinformaticsMathematicsInternal medicinePathologyBiology

Abstract

fetched live from OpenAlex

To the Editor: We appreciate the opportunity to respond to the letter by Dr. Maillard and Dr. Mariat (1Maillard N Mariat C. Solid-phase bead-based assays limitations are not restricted to interlaboratory variability.Am J Transplant. 2013; 13: 3049Abstract Full Text Full Text PDF PubMed Scopus (5) Google Scholar). They raise three questions regarding our recent article focusing on inter-laboratory standardization of solid phase multiplex-bead arrays to detect antibodies to HLA within the framework of the Clinical Trials in Organ Transplantation (2Reed EF, Rao P, Zhang Z, et al. Comprehensive assessment and standardization of solid phase multiplex-bead arrays for the detection of antibodies to HLA. Am J Transplant 13: 1859–1870.Google Scholar). Our article provides insights into the key sources of variability in commercially available solid phase HLA antibody testing kits. Importantly, our study demonstrates that standardization of reagents and protocols significantly reduces assay variance. Utilization of a global normalization algorithm further reduced median fluorescence intensity (MFI) variations in the protocol, thereby improving comparison of data across laboratories. Dr. Maillard and Dr. Mariat comment that our study did not address the prozone effect. Although they raise an important point, we do not believe the prozone effect impacted our estimates of inter-laboratory variance, since all participants adopted a standardized protocol, used the same reagents and tested a constant volume of alloantisera. We do agree with Dr. Maillard and Dr. Mariat that adding dithiothreitol, running dilutions or employing other methods to explore potential prozone/interfering factors is worthy of systematic investigation. Dr. Maillard and Dr. Mariat correctly point out that the %CV decreases within higher MFI strata. Although they indicate this finding is presented in the Bland–Altman plots (2, figure 5), it is actually illustrated in figure 3 of our article, which shows the variation among seven centers across distinct MFI strata. Dr. Maillard and Dr. Mariat suggest that the sudden amelioration in %CV within higher MFI strata is due to saturation of the beads with antibodies. However, as we clearly showed, the decline in %CV begins at 1000 MFI, well below a saturation dosage (<10 000 MFI), which indicates saturation is not the primary reason to explain this result. Their third point questions the impact of intra-laboratory variability on results and whether the improvement in %CV was due to a reduction in variance within an individual laboratory or between laboratories. Since it is standard of care that clinical laboratories utilize a standard operating procedure for HLA antibody testing, we expect the major cause of assay variance is lot-to-lot differences in test kits. Although we did not specifically address intra-laboratory variability in our report, each data point shown in the Bland–Altman plot (2, figure 5) can be converted into a pseudo “intra-laboratory” %CV [i.e. |ΔMFI|/(2×avgMFI)] representing the variation when a lab repeats the test of same sample and bead across two lots of single antigen kits. The median intra-laboratory %CV was 19%, and boxplots demonstrate a decline with increasing MFI range within each center and overall (Figure 1). On average, the intra-laboratory %CV was less than our reported inter-laboratory %CV (∼25%). Nonetheless, we acknowledge that other sources of variation in the aspects of the assay can certainly contribute to intra-laboratory variability and that each laboratory needs to address these concerns. We anticipate that both inter- and intra-laboratory variance will decrease with the implementation of standardized testing protocols and the increasing availability of uniform lots of reagents. This research was performed as part of an American Recovery and Reinvestment (ARRA) funded project under Award Number U0163594 (to P. Heeger), from the National Institute of Allergy and Infectious Diseases. The work was carried out by members of the Clinical Trials in Organ Transplantation (CTOT) and Clinical Trials in Organ Transplantation in Children (CTOT-C) consortia. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Allergy and Infectious Diseases or the National Institutes of Health. The authors of this manuscript have no conflicts of interest to disclose as described by the American Journal of Transplantation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.027
metaresearch head score (Gemma)0.097
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.027
Threshold uncertainty score0.141

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0270.097
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0030.002
Open science0.0030.001
Research integrity0.0080.009
Insufficient payload (model declined to judge)0.0020.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.025
GPT teacher head0.350
Teacher spread0.325 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations74
Published2013
Admission routes1
Has abstractyes

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