The impact of pre‐analytical variations on biochemical analytes stability: A systematic review
Bibliographic record
Abstract
OBJECTIVE: A common problem in clinical laboratories is maintaining the stability of analytes during pre-analytical processes. The aim of this study was to systematically summarize the results of a set of studies about the biochemical analytes stability. METHODS: A literature search was performed on the Advanced search field of PubMed using the keywords: "(stability) AND (analytes OR laboratory analytes OR laboratory tests OR biochemical analytes OR biochemical tests OR biochemical laboratory tests)." A total of 56 entries were obtained. After applying the selection criteria, 20 articles were included in the study. RESULTS: In the 20 included references, up to 123 different analytes were assessed. The 34 analytes in order of the most frequently studied analytes were evaluated: Alanine aminotransferase, aspartate aminotransferase, potassium, triglyceride, alkaline phosphatase, creatinine, total cholesterol, albumin, lactate dehydrogenase, sodium, calcium, γ-glutamyltransferase, total bilirubin, urea, creatine kinase, inorganic phosphate, total protein, uric acid, amylase, chloride, high-density lipoprotein, magnesium, glucose, C-reactive protein, bicarbonate, ferritin, iron, lipase, transferrin, cobalamin, cortisol, folate, free thyroxine, and thyroid-stimulating hormone. Stable test results could be varied between 2 hours and 1 week according to the type of samples and/or type of blood collection tubes on a basic classification set as refrigerated or room temperature. CONCLUSIONS: Biochemical analytes stability could be improved if the best pre-analytical approaches are used.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".