MétaCan
Menu
Back to cohort
Record W2534199374 · doi:10.1373/jalm.2016.021642

N-Terminal Pro–B-Type Natriuretic Peptide (NT-proBNP) Immunoreactivity Is Reduced After 6 Years of Storage at −70 °C

2016· article· en· W2534199374 on OpenAlexaff
Tracee Wee, Mila Tang, Ilinka Zrno, Jonah Hamilton, Daniel T. Holmes

Bibliographic record

VenueThe Journal of Applied Laboratory Medicine · 2016
Typearticle
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsSt. Paul's HospitalUniversity of British Columbia
Fundersnot available
KeywordsInterquartile rangeNatriuretic peptideMedicineInternal medicineSignificant difference

Abstract

fetched live from OpenAlex

BACKGROUND: Clinical trial samples may be stored frozen for prolonged periods before analysis, which can reduce the immunoreactivity of numerous analytes, particularly peptides. We sought to determine the effect of 6 years of frozen storage on serum N-terminal pro-B-type natriuretic peptide (NT-proBNP). METHODS: NT-proBNP was measured from serum samples taken from 99 different patients enrolled in the CanPREDDICT study after <1 year of storage at -70 °C using the Roche first-generation NT-proBNP assay on an e411 instrument. Separate aliquots of the same samples were analyzed after an additional 6 years of storage at -70 °C using the Roche second-generation assay on an e601 instrument. RESULTS: The median NT-proBNP immunoreactivity for the first measurement was 572 pg/mL (interquartile range [IQR] 205-1606, range 49-12820), while after an additional 6 years of storage at -70 °C, this value decreased to 526 pg/mL (IQR 181-1338, range 18-12880), resulting in a median percent difference of -7% (IQR -10.6% to -3.4%, P < 0.001). CONCLUSIONS: We report findings consistent with trends seen in previous work but have investigated the effect of a much longer storage period. Larger percent decreases in NT-proBNP reaching statistical significance are seen, although the median difference is still <10%.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.537
Threshold uncertainty score0.884

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.013
GPT teacher head0.261
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 designBench or experimental
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

Citations3
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of Applied Laboratory MedicineSame topicHeart Failure Treatment and ManagementFrench-language works237,207