Evidence map›Paper›PMID 33783617›Full record

ArticleChild's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery2021

Application of clinical prediction modeling in pediatric neurosurgery: a case study.

Hendrik-Jan Mijderwijk, Thomas Beez, Daniel Hänggi, Daan Nieboer

Abstract read
In one paragraph

Article in Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery, 2021. 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. Evolution of Making Clinical Predictions in Neurosurgery.Advances and technical standards in neurosurgery · 2023
    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

4 authors.

Hendrik-Jan MijderwijkMedical Faculty, Department of Neurosurgery, Heinrich Heine University, Moorenstraße 5, 40225, Düsseldorf, Germany. Hendrik-Jan.Mijderwijk@med.uni-duesseldorf.de.ORCID http://orcid.org/0000-0002-9516-8257
Thomas BeezMedical Faculty, Department of Neurosurgery, Heinrich Heine University, Moorenstraße 5, 40225, Düsseldorf, Germany.
Daniel HänggiMedical Faculty, Department of Neurosurgery, Heinrich Heine University, Moorenstraße 5, 40225, Düsseldorf, Germany.
Daan NieboerDepartment of Public Health, Erasmus University Medical Center, Rotterdam, The Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

There has been an increasing interest in articles reporting on clinical prediction models in pediatric neurosurgery. Clinical prediction models are mathematical equations that combine patient-related risk factors for the estimation of an individual's risk of an outcome. If used sensibly, these evidence-based tools may help pediatric neurosurgeons in medical decision-making processes. Furthermore, they may help to communicate anticipated future events of diseases to children and their parents and facilitate shared decision-making accordingly. A basic understanding of this methodology is incumbent when developing or applying a prediction model. This paper addresses this methodology tailored to pediatric neurosurgery. For illustration, we use original pediatric data from our institution to illustrate this methodology with a case study. The developed model is however not externally validated, and clinical impact has not been assessed; therefore, the model cannot be recommended for clinical use in its current form.

Indexed as

NeurosurgeryChildClinical Decision-MakingForecastingHumansNeurosurgeonsNeurosurgical ProceduresClinical prediction modelingPediatric neurosurgeryRisk assessment

Identifiers

PMID33783617
PMCPMC8084798

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.