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Record W4414740829 · doi:10.1093/clinchem/hvaf086.486

B-088 Stability Assessment of Hormones and Tumor Markers: Can We Ensure High-Quality Patient Results Despite Operational Challenges?

2025· article· en· W4414740829 on OpenAlexaff
Veni Bharti, Donna Stanley, Jason L. Robinson

Bibliographic record

VenueClinical Chemistry · 2025
Typearticle
Languageen
FieldMedicine
TopicPituitary Gland Disorders and Treatments
Canadian institutionsHealth PEI
Fundersnot available
KeywordsAnalyteCarcinoembryonic antigenStaffingReferralVolunteerParathyroid hormoneThyroid diseaseClinical Practice

Abstract

fetched live from OpenAlex

Abstract Background Clinical laboratory results play an important role in diagnosis, monitoring disease progression, and making informed decisions about the treatment plans of patients. The total testing process can be divided into three phases: pre-analytical, analytical, and post-analytical. The accuracy of laboratory results depends on all three phases of the testing process, however, the pre-analytical phase is a major source of error for clinical labs because it is most difficult to monitor and control. In centralized or referral labs it is important to consider operational challenges in the preanalytical phase such as transport delays and limited staffing that may delay sample analysis beyond stability limits provided by vendors or the literature. Therefore, it is prudent that labs define analyte stability limits based on time and storage conditions to ensure that only clinically actionable results are provided to clinicians. Methods Residual and volunteer samples were collected for Alpha-fetoprotein, Free T4, Free T3, Estradiol, Progesterone, Parathyroid hormone, Cortisol, Beta Human Chorionic Gonadotropin, Prostate-specific Antigen, Thyroid Peroxidase, Carcinoembryonic antigen, Cancer Antigen 125, Testosterone, Follicle Stimulating Hormone, Luteinizing Hormone, Dehydroepiandrosterone sulphate, Prolactin, Thyroid Stimulating Hormone, Vitamin B12, and Ferritin in both pathologic and non-pathologic concentrations to assess stability for 24 and 72 hours. Samples were collected in serum separator tubes (SST) or red-top vacutainers and stored refrigerated in original vacutainers (i.e. stored on cells or gel) or serum aliquots. Stability was assessed by comparing the percent deviation at each time point relative to the baseline result, and interpreted relative to the maximum permissible instability (MPI) using inter and intra-individual biological variation data from the European Federation of Clinical Chemistry and Lab Medicine (EFLM) database. MPI was calculated as: 0.375 v(Cvi2+ CVg2). Results Most of the hormones and tumor markers studied were stable in pathologic and non-pathologic ranges for aliquot samples and the original vacutainers across all time points (Table 1). Vitamin B12 exceeded stability limits by 72 h time point in original vacutainers and serum aliquots. Estradiol was stable for only 24 h of refrigerated storage in the original vacutainer only and these samples should not be sent as an aliquot. Conclusion This study supports an evidence-based process for receiving and storing specimens collected for hormone and tumor markers analysis. These data are useful for all clinical labs to provide accurate patient results when possible and to reduce unnecessary sample rejections and repeated phlebotomy.

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)0.001
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 score0.027
Threshold uncertainty score0.476

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.000
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 head0.060
GPT teacher head0.387
Teacher spread0.327 · 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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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