Evidence map›Paper›PMID 35252947›Full record

ArticleJournal of mass spectrometry and advances in the clinical lab2022

Indirect reference intervals using an R pipeline.

Dustin R Bunch

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of mass spectrometry and advances in the clinical lab, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Clinical Pathology and the Data Science revolution.Journal of mass spectrometry and advances in the clinical lab · 2022
    Article
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.

Dustin R BunchNationwide Children's Hospital, Department of Pathology and Laboratory Medicine, 700 Children's Dr, Columbus, OH 43205, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIndirect reference intervals require robust statistical approaches to separate the pathological and healthy values. This can be achieved with a data pipeline created in R, a freely available statistical programming language.

methodsA data pipeline was created to ingest, partition, normalize, remove outliers, and identify reference intervals for testosterone (Testo; n  = 7,207) and aspartate aminotransferase (AST; n  = 5,882) using data sets from NHANES.

resultsThe estimates for AST and Testo determined by this pipeline approximated current RIs. Care should be taken when using this pipeline as there are limitations that depend on the pathology of the analyte and the data set being used for RI estimation.

conclusionsR can be used to create a robust statistical reference interval pipeline.

Indexed as

ANOVA, Analysis of varianceAST, aspartate aminotransferaseCLSI, Clinical Laboratory Standards InstituteEHR, electronic health recordIFCC, International Federation of Clinical Chemistry and Laboratory MedicineLC-MS/MS, Liquid chromatography tandem mass spectrometryLIS, Laboratory informatics systemMixtoolsReference intervalRI, reference intervalR markdown tutorialSDI, Standard deviation indexSDR, Standard deviation ratioTesto, TestosteroneTukeyHSD, Tukey multiple pairwise-comparisonsz5, Critical z-score

Identifiers

PMID35252947
PMCPMC8889237

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

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.