Evidence map›Paper›PMID 37670507›Full record

ArticleBriefings in bioinformatics2023

Letter to the editor: testing the effectiveness of MyPROSLE in classifying patients with lupus nephritis.

Emily L Leventhal, Andrea R Daamen, Peter E Lipsky

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In one paragraph

Article in Briefings in bioinformatics, 2023. 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, top 74% of its field
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, 0 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors at 1 institution in 1 country.

Emily L LeventhalAMPEL BioSolutions LLC and the RILITE Research Institute, Charlottesville, VA 22902, USA.ORCID 0000-0002-1602-634X
Andrea R DaamenAMPEL BioSolutions LLC and the RILITE Research Institute, Charlottesville, VA 22902, USA.
Peter E LipskyAMPEL BioSolutions LLC and the RILITE Research Institute, Charlottesville, VA 22902, USA.
Ampel BioSolutions (United States) · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Because of the clinical heterogeneity among patients with systemic lupus erythematosus (SLE), developing molecular profiles that predict clinical features can be useful in creating a personalized approach to treatment. Toro-Domínguez et al. created a web tool to aid in therapeutic decision making for clinicians that predicts clinical features associated with SLE from blood transcriptomic data. Specifically, they present a machine learning model that predicts the presence of proliferative nephritis from blood transcriptomics. Here, we report use of the tool in independent datasets and found that it did not perform sufficiently well to consider replacement of the standard kidney biopsy as a diagnostic procedure.

Indexed as

Lupus Erythematosus, SystemicLupus NephritisGene Expression ProfilingHumansMachine LearningTranscriptomelupus nephritismachine learningprecision medicinesystemic lupus erythematosus

Identifiers

PMID37670507
OpenAlexW4386466915

What OpenQuestion holds

Textmetadata
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.