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Record W4392447181 · doi:10.1515/cclm-2023-1192

The stability of 65 biochemistry analytes in plasma, serum, and whole blood

2024· article· en· W4392447181 on OpenAlexaff
Janet R. Zhou, Anselmo Fabros, Sarah Jane Lam, A. Coro, Rajeevan Selvaratnam, Davor Brinc, Ashley Di Meo

Bibliographic record

VenueClinical Chemistry and Laboratory Medicine (CCLM) · 2024
Typearticle
Languageen
FieldMedicine
TopicClinical Laboratory Practices and Quality Control
Canadian institutionsUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsVacutainerAnalytePhlebotomyCentrifugationChromatographyWhole bloodChemistryMedicineImmunologySurgery

Abstract

fetched live from OpenAlex

OBJECTIVES: The pre-analytical stability of various biochemical analytes requires careful consideration, as it can lead to the release of erroneous laboratory results. There is currently significant variability in the literature regarding the pre-analytical stability of various analytes. The aim of this study was to determine the pre-analytical stability of 65 analytes in whole blood, serum and plasma using a standardized approach. METHODS: -EDTA, or Na-fluoride/K-oxalate. Several conditions were tested, including delayed centrifugation with storage of whole blood at room temperature (RT) for 8 h, delayed centrifugation with storage of whole blood at RT or 4 °C for 24 h, and immediate centrifugation with storage of plasma or serum at RT for 24 h. Percent deviation (% PD) from baseline was calculated for each analyte and compared to the maximum permissible instability (MPI) derived from intra- and inter-individual biological variation. RESULTS: The majority of the analytes evaluated remained stable across all vacutainer types, temperatures, and timepoints tested. Glucose, potassium, and aspartate aminotransferase, among others, were significantly impacted by delayed centrifugation, having been found to be unstable in whole blood specimens stored at room temperature for 8 h. CONCLUSIONS: The data presented provides insight into the pre-analytical variables that impact the stability of routine biochemical analytes. This study may help to reduce the frequency of erroneous laboratory results released due to exceeded stability and reduce unnecessary repeat phlebotomy for analytes that remain stable despite delayed processing.

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)0.005
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.509
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
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 head0.044
GPT teacher head0.384
Teacher spread0.340 · 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

Citations24
Published2024
Admission routes1
Has abstractyes

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