MétaCan
Menu
Back to cohort
Record W2167904600 · doi:10.1373/clinchem.2014.223495

Hyperbilirubinemia in Anicteric Blood?

2014· article· en· W2167904600 on OpenAlexaff
Yu Chen, Lauren Graham, Ihssan Bouhtiauy, Mary Kristina Hamilton

Bibliographic record

VenueClinical Chemistry · 2014
Typearticle
Languageen
FieldMedicine
TopicNeonatal Health and Biochemistry
Canadian institutionsVitalité Health NetworkHorizon Health NetworkDr. Everett Chalmers Regional HospitalUpper River Valley HospitalDalhousie University
Fundersnot available
KeywordsMedicine

Abstract

fetched live from OpenAlex

Plasma total bilirubin for a 65-year-old man was high [19.1 mg/dL (326 μmol/L)] on a Roche cobas 6000 analyzer. However, the specimen was anicteric and the icteric index was 1 (approximately 1 mg/dL or 17 μmol/L). A dilution study demonstrated nonlinear results. Direct bilirubin was measured as normal. The split specimen aliquots were further measured on analyzers from 5 different manufacturers (Table 1). The patient's total protein and albumin were 93 and 34 g/L (reference intervals: 60–80 and 38–50 g/L). What might cause artifactual hyperbilirubinemia in anicteric blood? What strategies can be employed to deal with spurious increased bilirubin results? The answers are below. The patient's serum IgG, IgM, and IgA concentrations were 3.9, 0.1, and 39.2 g/L, respectively. Protein electrophoresis revealed an IgA-λ paraprotein (38.3 g/L), a small Bence Jones λ in serum, and a prominent Bence Jones λ (27.3 g/day) in urine. IgG and IgM paraproteins have been suggested to interfere with total bilirubin assays, especially on Roche analyzers, by forming precipitants (1–5). In this case, IgA-λ and possibly Bence Jones λ involvement were indicated. To deal with pseudo-hyperbilirubinemia, naked eye examinations, spectrophotometric indices, different analytical measurements, and the clinical picture will be helpful (1).

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.597
Threshold uncertainty score0.458

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.024
GPT teacher head0.362
Teacher spread0.339 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueClinical ChemistrySame topicNeonatal Health and BiochemistryFrench-language works237,207