Evidence map›Paper›PMID 35036991›Full record

ArticleFrontiers in pain research (Lausanne, Switzerland)2021

Unsupervised Machine Learning Approaches Reveal Distinct Phenotypes of Perceived Bladder Pain: A Pilot Study.

Patricia J Mwesigwa, Nicholas J Jackson, Ashley T Caron, Falisha Kanji, James E Ackerman, Jessica R Webb, Victoria C S Scott, Karyn S Eilber, David M Underhill, Jennifer T Anger and 1 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in pain research (Lausanne, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.3field-weighted citation impact, top 20% 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

5 citing papers in PubMed, 9 citations in OpenAlex.

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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

11 authors at 2 institutions in 1 country.

Patricia J MwesigwaDepartment of Obstetrics and Gynecology, Center for Women's Pelvic Health, David Geffen School of Medicine at UCLA, Los Angeles, CA, United States.
Nicholas J JacksonDepartment of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, United States.
Ashley T CaronDivision of Urology, Department of Surgery, Cedars-Sinai Medical Center, Los Angeles, CA, United States.
Falisha KanjiDivision of Urology, Department of Surgery, Cedars-Sinai Medical Center, Los Angeles, CA, United States.
James E AckermanDivision of Urology, Department of Surgery, Cedars-Sinai Medical Center, Los Angeles, CA, United States.
Jessica R WebbCedars-Sinai Medical Group, Department of Internal Medicine, Los Angeles, CA, United States.
Victoria C S ScottDivision of Urology, Department of Surgery, Cedars-Sinai Medical Center, Los Angeles, CA, United States.
Karyn S EilberDivision of Urology, Department of Surgery, Cedars-Sinai Medical Center, Los Angeles, CA, United States.
David M UnderhillDepartment of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, United States.
Jennifer T AngerDivision of Urology, Department of Surgery, Cedars-Sinai Medical Center, Los Angeles, CA, United States.
A Lenore AckermanDivision of Pelvic Medicine and Reconstructive Surgery, Department of Urology, David Geffen School of Medicine at UCLA, Los Angeles, CA, United States.
Cedars-Sinai Medical Center · USUniversity of California, Los Angeles · US

Funding

The Urinary Microbiota and Host Inflammation in Lower Urinary Tract SymptomsK08DK118176 · NIDDK · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI ACKERMAN, A. LENORE · 2019 to 2023
$839k
NIDDK NIH HHS K08 DK118176
6 · The paper itself

Abstract

Interstitial cystitis/bladder pain syndrome (IC/BPS) is defined as an unpleasant sensation perceived to be related to the bladder with associated urinary symptoms. Due to difficulties discriminating pelvic visceral sensation, IC/BPS likely represents multiple phenotypes with different etiologies that present with overlapping symptomatic manifestations, which complicates clinical management. We hypothesized that unique bladder pain phenotypes or "symptomatic clusters" would be identifiable using machine learning analysis (unsupervised clustering) of validated patient-reported urinary and pain measures. Patients (

Indexed as

bladder pain syndromeinterstitial cystitislower urinary tract symptomspelvic pain/discomfortphenotypesunsupervised machine learningurinary symptoms

Identifiers

PMID35036991
PMCPMC8758057
OpenAlexW3213466466

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.