Evidence map›Paper›PMID 37768336›Full record

ArticleEuropean spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society2023

Pre-operative prediction of post-operative urinary retention in lumbar surgery: a prospective validation of machine learning model.

Ken Porche, Carolina B Maciel, Brandon Lucke-Wold, Yusuf Mehkri, Yasmeen Murtaza, Michael Goutnik, Steven A Robicsek, Katharina M Busl

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In one paragraph

Article in European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society, 2023. 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. 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

8 authors.

Ken PorcheDepartment of Neurosurgery, University of Florida, PO Box 100265, Gainesville, FL, 32610, USA. ken.porche@neurosurgery.ufl.edu.
Carolina B MacielDepartment of Neurosurgery, University of Florida, PO Box 100265, Gainesville, FL, 32610, USA.
Brandon Lucke-WoldDepartment of Neurosurgery, University of Florida, PO Box 100265, Gainesville, FL, 32610, USA.
Yusuf MehkriCollege of Medicine, University of Florida, Gainesville, FL, USA.ORCID 0000-0002-3129-465X
Yasmeen MurtazaCollege of Medicine, University of Florida, Gainesville, FL, USA.
Michael GoutnikCollege of Medicine, University of Florida, Gainesville, FL, USA.
Steven A RobicsekDepartment of Anesthesiology, University of Florida, Gainesville, FL, USA.
Katharina M BuslDepartment of Neurosurgery, University of Florida, PO Box 100265, Gainesville, FL, 32610, USA.

Funding

Using social networks to map and evaluate team science across CTSA hubsUL1TR001427 · NCATS · UNIVERSITY OF FLORIDA · PI MITCHELL, DUANE A. · 2015 to 2024
$37.2M
NCATS NIH HHS UL1 TR001427
6 · The paper itself

Abstract

purposePredicting urinary retention is difficult. The aim of this study is to prospectively validate a previously developed model using machine learning techniques.

methodsPatients were recruited from pre-operative clinic. Prediction of urinary retention was completed pre-operatively by 4 individuals and compared to ground truth POUR outcomes. Inter-rater reliability was calculated with intercorrelation coefficient (2,1).

results171 patients were included with age 63 ± 14 years, 58.5% (100/171) male, BMI 30.4 ± 5.9 kg/m

conclusionsThis prospective study confirms performance of the prediction model for POUR developed with retrospective data, showing great correlation. This supports the use of machine learning techniques in the prediction of postoperative complications such as urinary retention.

Indexed as

Urinary RetentionAgedHumansMachine LearningMaleMiddle AgedPostoperative ComplicationsProspective StudiesReproducibility of ResultsRetrospective StudiesLumbar spine surgeryMachine learningPrediction toolUrinary retention

Identifiers

PMID37768336
PMCPMC11418914

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Registered trials

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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.