ReviewBiochemia medica2020
Statistical distributions commonly used in measurement uncertainty in laboratory medicine.
Review in Biochemia medica, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 27 citations in OpenAlex.
- A coma pattern-based autofocusing method resolves bacterial cold shock response at single-cell level.eLife · 2026Article
- A Practical Guide to Assess Data Distribution and Normality with Open-Source Software.Indian dermatology online journal · 2026Article
- The prognostic significance of epoxide hydrolases in colorectal cancer.Biochemistry and biophysics reports · 2025Article
- Diagnosis Based on Population Data versus Personalized Data: The Evolving Paradigm in Laboratory Medicine.Diagnostics (Basel, Switzerland) · 2024Review
- Bias in Laboratory Medicine: The Dark Side of the Moon.Annals of laboratory medicine · 2024Review
- Cost-Effectiveness of Drug-Coated Balloon Angioplasty versus Plain Old Balloon Angioplasty for Arteriovenous Fistula Stenosis.Cardiovascular and interventional radiology · 2023Article
- Bias, the unfinished symphony.Biochemia medica · 2022Article
- Uric Acid Variability as a Predictive Marker of Newly Developed Cardiovascular Events in Type 2 Diabetes.Frontiers in cardiovascular medicine · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Uncertainty is an inseparable part of all types of measurement. Recently, the International Organization for Standardization (ISO) released a new standard (ISO 20914) on how to calculate measurement uncertainty (MU) in laboratory medicine. This standard can be regarded as the beginning of a new era in laboratory medicine. Measurement uncertainty comprises various components and is used to calculate the total uncertainty. All components must be expressed in standard deviation (SD) and then combined. However, the characteristics of these components are not the same; some are expressed as SD, while others are expressed as a ± b, such as the purity of the reagents. All non-SD variables must be transformed into SD, which requires a detailed knowledge of common statistical distributions used in the calculation of MU. Here, the main statistical distributions used in MU calculation are briefly summarized.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.