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Record W2323558274 · doi:10.3109/00365513.2016.1143113

Heterophilic antibody interference affecting multiple hormone assays: Is it due to rheumatoid factor?

2016· article· en· W2323558274 on OpenAlexaff
Shiva Mongolu, Annie Armston, Erin Mozley, Azraai Bahari Nasruddin

Bibliographic record

VenueScandinavian Journal of Clinical and Laboratory Investigation · 2016
Typearticle
Languageen
FieldMedicine
TopicMonoclonal and Polyclonal Antibodies Research
Canadian institutionsSt. Thomas Hospital
Fundersnot available
KeywordsRheumatoid arthritisRheumatoid factorMedicineInternal medicineHormoneAntibodyEndocrinologyProlactinEndocrine systemImmunology

Abstract

fetched live from OpenAlex

Assay interference with heterophilic antibodies has been well described in literature. Rheumatoid factor is known to cause similar interference leading to falsely elevated hormone levels when measured by immunometric methods like enzyme-linked immunosorbent assay (ELISA) or multiplex immunoasays (MIA). We report a case of a 60-year-old male patient with a history of rheumatoid arthritis referred to our endocrine clinic for investigation of hypogonadism and was found to have high serum levels of LH, FSH, SHBG, Prolactin, HCG and TSH. We suspected assay interference and further tests were performed. We used Heteroblock tubes and PEG precipitation to eliminate the interference and the hormone levels post treatment were in the normal range. We believe the interference was caused by high serum levels of rheumatoid factor. Although he was treated with thyroxine for 3 years, we believe he may have been treated inappropriately as his Free T4 level was always normal despite high TSH due to assay interference. Our case illustrates the phenomenon of heterophilic antibody interference likely due to high levels of rheumatoid factor. It is essential for clinicians and endocrinologists in particular to be aware of this possibility when making treatment decisions in these groups of patients.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.116
Threshold uncertainty score0.473

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.090
GPT teacher head0.401
Teacher spread0.311 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations25
Published2016
Admission routes1
Has abstractyes

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