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Record W4384819630 · doi:10.1111/vcp.13242

Methodology‐related pseudohyperkalemia associated with marked muscle enzyme leakage in a dog

2023· article· en· W4384819630 on OpenAlexaff
Noel Clancey, Shelley Burton, Cornelia Gilroy, Janet Saunders

Bibliographic record

VenueVeterinary Clinical Pathology · 2023
Typearticle
Languageen
FieldMedicine
TopicClinical Laboratory Practices and Quality Control
Canadian institutionsUniversity of Prince Edward Island
Fundersnot available
KeywordsHyperkalemiaPotassiumChemistryConfidence intervalInternal medicineLinear regressionCreatine kinaseEndocrinologyMedicineBiochemistry

Abstract

fetched live from OpenAlex

Abstract Background As hyperkalemia may be life‐threatening, it is critical to recognize artifactually increased potassium concentrations. Pseudohyperkalemia may occur in myopathies when using the VetScan2 analyzer (VS2), but the degree of pseudohyperkalemia and relationships relative to creatine kinase activity (CK) are unknown. Objectives We aimed to determine what degree of muscle enzyme leakage, as reflected by increased serum CK activity, results in cases with falsely elevated potassium concentrations when measured by the VS2. We also sought to establish if a linear relationship exists between potassium concentrations measured by the VS2 and CK activity. Methods Serum samples from dogs with increased CK activity and with CK activity within the reference interval and without clinically relevant biochemical alterations were used to create diluted samples having various CK activities. Potassium concentrations and CK activities were determined on VS2 and Cobas c501 (Cobas) analyzers. Wilcoxon signed rank, Bland–Altman, and Passing‐Bablok analyses were used to compare potassium concentrations generated by the VS2 and Cobas analyzers. Least squares regression analysis was performed to evaluate if a linear relationship exists between VS2 potassium concentrations and Cobas CK activities. Results Potassium concentrations from the VS2 were higher (median and standard deviation (SD) = 5.2 +/− 0.46 mmol/L) than those from the Cobas analyzer (4.4 +/− 0.35 mmol/L; P < 0.000), and a positive mean bias was found (mean difference = 0.774 mmol/L; 95% Confidence Interval (CI) = 0.706–0.842; limits of agreement = 0.21–1.34). Passing‐Bablok regression showed a positive proportional bias for potassium concentrations on the VS2 compared with paired Cobas results (Slope = 1.328; 95% CI = 1.100–1.500) but did not reveal systematic bias (Intercept = −0.714; 95% CI = −1.46–0.265). Least squares regression analysis showed a poor non‐significant relationship ( R 2 = 0.19) between potassium measured by the VS2 and CK measured by the Cobas analyzer. Conclusions A defined threshold value of CK activity at which potassium concentration begins to falsely increase when measured on the VS2 was not established as data widely varied. A poor non‐significant relationship between potassium concentrations and CK activities did not allow prediction of the threshold at which falsely increased potassium concentrations would be expected on the VS2.

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)0.011
metaresearch head score (Gemma)0.020
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.216
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.281
GPT teacher head0.476
Teacher spread0.195 · 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.

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
Published2023
Admission routes1
Has abstractyes

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