Evidence map›Paper›PMID 39658658›Full record

ArticleChild's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery2024

Does machine learning improve prediction accuracy of the Endoscopic Third Ventriculostomy Success Score? A contemporary Hydrocephalus Clinical Research Network cohort study.

Armaan K Malhotra, Abhaya V Kulkarni, Leonard H Verhey, Ron W Reeder, Jay Riva-Cambrin, Hailey Jensen, Ian F Pollack, Michael McDowell, Brandon G Rocque, Mandeep S Tamber and 15 more

Abstract read
PubMed Publisher
In one paragraph

Article in Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–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 synthesis or guideline pooled it.

  1. Machine learning for endoscopic third ventriculostomy success prediction-a systematic review and meta-analysis.Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery · 2025
    Pooled it
  2. Recalibrating ETV prediction: a standards-first, decision-aware, and equity-oriented roadmap for pediatric hydrocephalus.Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery · 2025
    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

25 authors.

Armaan K MalhotraDivision of Neurosurgery, Hospital for Sick Children, University of Toronto, Toronto, ON, Canada.
Abhaya V KulkarniDivision of Neurosurgery, Hospital for Sick Children, University of Toronto, Toronto, ON, Canada. abhaya.kulkarni@sickkids.ca.
Leonard H VerheyDivision of Neurosurgery, Department of Clinical Neurosciences, Spectrum Health, Michigan State University, Grand Rapids, MI, USA.
Ron W ReederDepartment of Pediatrics, University of Utah, Salt Lake City, UT, USA.
Jay Riva-CambrinDivision of Neurosurgery, Alberta Children's Hospital, University of Calgary, Calgary, AB, Canada.
Hailey JensenDepartment of Pediatrics, University of Utah, Salt Lake City, UT, USA.
Ian F PollackDepartment of Neurosurgery, UPMC Children's Hospital of Pittsburgh, University of Pittsburgh, Pittsburgh, PA, USA.
Michael McDowellDepartment of Neurosurgery, UPMC Children's Hospital of Pittsburgh, University of Pittsburgh, Pittsburgh, PA, USA.
Brandon G RocqueDepartment of Neurosurgery, Children's of Alabama, University of Alabama, Birmingham, AL, USA.
Mandeep S TamberDivision of Neurosurgery, UBC Department of Surgery, BC Children's Hospital, Vancouver, BC, Canada.
Patrick J McDonaldSection of Neurosurgery, Department of Surgery, University of Manitoba, Winnipeg, MB, Canada.
Mark D KriegerDepartment of Neurosurgery, Children's Hospital Los Angeles, Keck School of Medicine of the University of Southern California, Los Angeles, CA, USA.
Jonathan A PindrikDivision of Pediatric Neurosurgery, Department of Neurological Surgery, Nationwide Children's Hospital, The Ohio State University College of Medicine, Columbus, OH, USA.
Albert M IsaacsDivision of Pediatric Neurosurgery, Department of Neurological Surgery, Nationwide Children's Hospital, The Ohio State University College of Medicine, Columbus, OH, USA.
Jason S HauptmanDivision of Neurological Surgery, Phoenix Children's Hospital, Phoenix, USA.
Samuel R BrowdDepartment of Neurological Surgery, Seattle Children's Hospital, University of Washington School of Medicine, Seattle, WA, USA.
William E WhiteheadDepartment of Neurosurgery, Texas Children's Hospital, Baylor College of Medicine, Houston, TX, USA.
Eric M JacksonDepartment of Neurosurgery, Johns Hopkins Medicine, Baltimore, MD, USA.
John C WellonsDepartment of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA.
Todd C HankinsonDepartment of Neurosurgery, Children's Hospital Colorado, University of Colorado, Aurora, CO, USA.
Jason ChuDepartment of Neurosurgery, Riley Children's Health, Indiana University Health, Indianapolis, IN, USA.
David D LimbrickDepartment of Neurosurgery, Children's Hospital of Richmond, Virginia Commonwealth University Health, Richmond, VA, USA.
Jennifer M StrahleDepartment of Neurosurgery, St. Louis Children's Hospital, Washington University School of Medicine, St. Louis, St. Louis, MO, USA.
John R W KestleDepartment of Neurosurgery, University of Utah, Salt Lake City, UT, USA.
Hydrocephalus Clinical Research Network

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThis Hydrocephalus Clinical Research Network (HCRN) study had two aims: (1) to compare the predictive performance of the original ETV Success Score (ETVSS) using logistic regression modeling with other newer machine learning models and (2) to assess whether inclusion of imaging variables improves prediction performance using machine learning models.

methodsWe identified children undergoing first-time ETV for hydrocephalus that were enrolled prospectively at HCRN sites between 200 and 2020. The primary outcome was ETV success 6 months after index surgery. The cohort was randomly divided into training (70%) and testing (30%) datasets. The classic ETVSS variables were used for logistic regression and machine learning models. Predictive performance of each model was evaluated on the testing dataset using area under the receiver operating characteristic curve (AUROC).

resultsThere were 752 patients that underwent first time ETV, of which 185 patients (24.6%) experienced ETV failure within 6 months. For aim 1, using the classic ETVSS variables, machine learning models did not outperform logistic regression with AUROC 0.60 (95% CI: 0.52-0.69) for Naïve Bayes (highest machine learning model performance) and 0.68 (95% CI: 0.60-0.76) for logistic regression. After inclusion of imaging features (aim 2), machine learning model prediction improved but remained no better than the above logistic regression with the highest AUROC of 0.67 (95% CI: 0.59-0.75) attained using Naïve Bayes architecture compared to 0.68 (95% CI: 0.59-0.76) for logistic regression.

conclusionsThis contemporary multicenter observational cohort study demonstrated that machine learning modeling strategies did not improve performance of the ETVSS model over logistic regression.

Indexed as

HydrocephalusMachine LearningThird VentricleVentriculostomyChildChild, PreschoolCohort StudiesFemaleHumansInfantMaleNeuroendoscopyTreatment OutcomeEndoscopic third ventriculostomyHydrocephalusMachine learningPediatricSuccess score

Identifiers

PMID39658658

What OpenQuestion holds

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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.