ReviewInternational journal of laboratory hematology2026
Reproducible Data Analysis With R in Laboratory Hematology.
Review in International journal of laboratory hematology, 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
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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
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Laboratory hematology generates large, complex datasets that increasingly exceed the capabilities of traditional spreadsheet-based analytical workflows. Despite their familiarity, spreadsheets obscure analytical logic, encourage manual data manipulation, and lack durable records of analytical steps, limiting transparency, auditability, and reproducibility. These limitations present specific challenges in clinical laboratory settings subject to regulatory oversight. Script-based data analysis offers a practical alternative by encoding analytical workflows as explicit, ordered, and reusable operations. This review outlines the structural limitations of spreadsheet-centered analysis, illustrates how these constraints contribute to analytical error, and introduces script-based analysis using R as a reproducible framework suited to laboratory hematology. Practical applications are highlighted across method comparison, reference interval estimation, quality control, flow cytometry, and operational monitoring. The discussion also includes the importance of complementary technologies like integrated development environments, version control, and large language models. Adoption of reproducible, script-based analytical tools such as R represents a foundational step toward improving data integrity, analytical rigor, and readiness for advanced analytical techniques in laboratory hematology.
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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.