ArticleEJIFCC2026
Toward Standardization of Laboratory Terminology.
Article in EJIFCC, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Laboratory medicine is fundamental to evidence-based healthcare, providing critical data on human properties for diagnosing and managing diseases. The increasing complexity and size of laboratory data necessitate robust information technology (IT) systems for efficient management. Purpose: This manuscript elucidates the roles and infrastructure requirements of laboratory IT systems by stating the purposes of laboratory medicine and examining the challenges and prerequisites associated with their achievement. Methods: We explore the primary purpose of laboratory medicine in supporting clinical decisions and the opportunities for secondary data use. The challenges of data heterogeneity, insufficient and lack of metadata, and the need for standardized terminologies are addressed. We further emphasize the prerequisites including international collaboration and education. Results: Technological advancements have enabled automation in laboratories, reducing errors, improving quality, and increasing efficiency and capacity for 24/7 sample processing. IT systems facilitate rapid results, enhance resource allocation, and manage the entire testing process from ordering to result interpretation. The establishment of two coherent and comprehensive laboratory information models - one for communication in the laboratory and another for communication between diverse health care providers - is proposed as essential for accurate data exchange and maximizing data utility. Conclusion: Achieving successful exchange of laboratory results and optimal use of laboratory data hinge on international collaboration, shared resource commitment, and an approach to standardised terminologies and data structures. This work lays the foundation for ongoing efforts to develop international laboratory information models, critical for advancing patient care and leveraging the full potential of laboratory medicine.
Indexed as
Identifiers
42699471PMC13543440What 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.