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Record W3089554330 · doi:10.1093/jalm/jfaa102

Inaccuracy of Sodium Measurement in Patients with Severe Hypernatremia

2020· article· en· W3089554330 on OpenAlexafffund
Amir Karin, Davor Brinc, Felix Leung, Benjamin Jung

Bibliographic record

VenueThe Journal of Applied Laboratory Medicine · 2020
Typearticle
Languageen
FieldMedicine
TopicClinical Laboratory Practices and Quality Control
Canadian institutionsHospital for Sick ChildrenSinai Health SystemUniversity Health NetworkUniversity of Toronto
FundersUniversity Health Network
KeywordsHypernatremiaSodiumMedicineMaterials scienceMetallurgy

Abstract

fetched live from OpenAlex

INTRODUCTION: We observed discordant sodium results from a patient with severe hypernatremia such that whole-blood analysis produced results up to 9.6 mmol/L higher than plasma sodium obtained on the same collection. We investigated this bias by comparing other patients' sodium results and performing comparisons of 3 blood gas and 2 chemistry analyzers. METHODS: First, the laboratory information system was queried for whole-blood sodium results >160 mmol/L, which were used for comparison against plasma results from the same collection. Second, whole blood was collected from a healthy donor, a portion of which was spiked with sodium chloride to generate 8 samples with target concentrations of 140 to 185 mmol/L. Whole-blood sodium was measured in duplicate on the ABL90, RAPIDPoint 500, and GEM 4000. Plasma sodium was then measured in duplicate on the Architect c8000 and Cobas c702. Finally, plasma was injected on the blood gas analyzers to measure sodium in singleton. RESULTS: Overall, 53 paired results from patients showed a significant positive bias on the ABL90 relative to Vitros when sodium was >160 mmol/L. The magnitude of difference was insignificant within the reference range but increased proportionately with concentration. The magnitude and pattern of positive bias in ABL90 sodium results were consistent with the observation in patient results. CONCLUSION: In severe hypernatremia, sodium results produced by blood gas and plasma analyzers can differ significantly.

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)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.183
Threshold uncertainty score0.366

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.041
GPT teacher head0.289
Teacher spread0.248 · 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

Citations1
Published2020
Admission routes2
Has abstractyes

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