Evidence map›Paper›PMID 40525063›Full record

ArticleCureus2025

Normalization of Reference Ranges for Laboratory Tests Using a Novel Rescaling Approach for Optimal Clinical Interpretation.

Dhayananth Rattaipalivalasu Saravanan

Abstract read
In one paragraph

Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Dhayananth Rattaipalivalasu SaravananInternal Medicine, Mercy Health St. Vincent Medical Center, Toledo, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Laboratory result interpretation is often hindered by variability in reference ranges across different institutions and analytes. This inconsistency can delay clinical decisions and increase cognitive effort. We present a normalization method that transforms laboratory values into dimensionless scores using simple rescaling formulas based on the reference range. By converting values to a 0 to 1 or -1 to 1 scale, this approach allows intuitive assessment and direct comparison across different tests, regardless of their original units. It simplifies recognition of abnormal values, enhances clinical efficiency, and is suitable for integration into electronic health records and algorithmic tools. Applied to common laboratory tests, the method demonstrated improved interpretability and clarity. Normalizing lab values in this way offers a practical, scalable solution to support faster and more accurate clinical decision-making.

Indexed as

ai and machine learningapplications of medical informaticsdata standardizationelectronic clinical decision support systemelectronic health records (ehr)laboratory interpretationlaboratory normalizationreference rangerescaling methodstandardized lab values

Identifiers

PMID40525063
PMCPMC12168664

What OpenQuestion holds

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Registered trials

None linked

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.