Evidence map›Paper›PMID 38160292›Full record

ArticlePacific Symposium on Biocomputing. Pacific Symposium on Biocomputing2024

Cluster Analysis reveals Socioeconomic Disparities among Elective Spine Surgery Patients.

Alena Orlenko, Philip J Freda, Attri Ghosh, Hyunjun Choi, Nicholas Matsumoto, Tiffani J Bright, Corey T Walker, Tayo Obafemi-Ajayi, Jason H Moore

Abstract read
In one paragraph

Article in Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 2024. 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

9 authors.

Alena OrlenkoDepartment of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, California, USA*These authors contributed equally to the paper.
Philip J Freda
Attri Ghosh
Hyunjun Choi
Nicholas Matsumoto
Tiffani J Bright
Corey T Walker
Tayo Obafemi-Ajayi
Jason H Moore

Funding

Bioinformatics Strategies for Genome-Wide Association StudiesR01LM010098 · NLM · UNIVERSITY OF PENNSYLVANIA · PI MOORE, JASON H., WILLIAMS, SCOTT MATTHEW · 2009 to 2023
$5.1M
NLM NIH HHS R01 LM010098
6 · The paper itself

Abstract

This work demonstrates the use of cluster analysis in detecting fair and unbiased novel discoveries. Given a sample population of elective spinal fusion patients, we identify two overarching subgroups driven by insurance type. The Medicare group, associated with lower socioeconomic status, exhibited an over-representation of negative risk factors. The findings provide a compelling depiction of the interwoven socioeconomic and racial disparities present within the healthcare system, highlighting their consequential effects on health inequalities. The results are intended to guide design of fair and precise machine learning models based on intentional integration of population stratification.

Indexed as

MedicareSocioeconomic Disparities in HealthAgedCluster AnalysisComputational BiologyHumansRacial GroupsUnited States

Identifiers

PMID38160292
PMCPMC11250986

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.