Evidence map›Paper›PMID 40903733›Full record

ArticleBMC medical informatics and decision making2025

Comparing supervised machine learning algorithms for the prediction of partial arterial pressure of oxygen during craniotomy.

Andrea S Gutmann, Maximilian M Mandl, Clemens Rieder, Dominik J Hoechter, Konstantin Dietz, Benjamin P Geisler, Anne-Laure Boulesteix, Roland Tomasi, Ludwig C Hinske

Abstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC medical informatics and decision making, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

9 authors.

Andrea S GutmannDepartment of Anaesthesiology, LMU University Hospital, LMU Munich, Munich, Germany.
Maximilian M MandlInstitute for Medical Information Processing, Biometry and Epidemiology (IBE), Faculty of Medicine, LMU Munich, Munich, Germany.
Clemens RiederDepartment of Anaesthesiology, LMU University Hospital, LMU Munich, Munich, Germany.
Dominik J HoechterDepartment of Anaesthesiology, LMU University Hospital, LMU Munich, Munich, Germany.
Konstantin DietzDepartment of Anaesthesiology, LMU University Hospital, LMU Munich, Munich, Germany.
Benjamin P GeislerInstitute for Medical Information Processing, Biometry and Epidemiology (IBE), Faculty of Medicine, LMU Munich, Munich, Germany.
Anne-Laure BoulesteixInstitute for Medical Information Processing, Biometry and Epidemiology (IBE), Faculty of Medicine, LMU Munich, Munich, Germany.
Roland TomasiDepartment of Anaesthesiology, LMU University Hospital, LMU Munich, Munich, Germany.
Ludwig C HinskeDepartment of Anaesthesiology, LMU University Hospital, LMU Munich, Munich, Germany. chinske@alum.mit.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND AND

objectivesBrain tissue oxygenation is usually inferred from arterial partial pressure of oxygen (paO

methodsData from routine clinical care of lung-healthy neurosurgical patients were extracted from databases of the respective clinical systems and normalized. We used recursive feature elimination to identify relevant features for the prediction of paO

resultsWe analyzed N = 4,581 patients with n = 17,821 observations. Between 5 and 22 features were selected from the analysis of the training dataset comprising 3,436 patients with 13,257 observations. The best algorithm, a regularized linear model with stochastic gradient descent, could predict paO

conclusionPaO

Indexed as

AlgorithmsArterial PressureCraniotomyMonitoring, IntraoperativeOxygenSupervised Machine LearningAdultAgedFemaleHumansMaleMiddle AgedPartial PressureOxygenArterial partial pressure of oxygenBlood gas analysisCraniotomyMachine learningpaO2

Identifiers

PMID40903733
PMCPMC12406590

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