Evidence map›Paper›PMID 40597440›Full record

ArticleJournal of anesthesia, analgesia and critical care2025

Different artificial neural networks for predicting burnout risk in Italian anesthesiologists.

Marco Cascella, Alessandro Simonini, Sergio Coluccia, Elena Giovanna Bignami, Gilberto Fiore, Emiliano Petrucci, Alessandro Vergallo, Giacomo Sollecchia, Franco Marinangeli, Roberto Pedone and 1 more

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In one paragraph

Article in Journal of anesthesia, analgesia and critical care, 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

11 authors.

Marco CascellaUnit of Anesthesiology, Intensive Care Medicine, and Pain Medicine, Department of Medicine, Surgery, and Dentistry, University of Salerno, Baronissi, Salerno, 84082, Italy.
Alessandro Simonini *Pediatric Anesthesia and Intensive Care Unit, Salesi Children's Hospital, 60121, Ancona, Italy.
Sergio Coluccia *Branch of Medical Statistics, Biometry and Epidemiology "G. A. Maccacaro", Department of Clinical Sciences and Community Health, Dipartimento di Eccellenza 2023-2027, Università degli Studi di Milano, Milan, 20133, Italy.
Elena Giovanna BignamiAnesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Parma, 43121, Italy.
Gilberto FioreDepartment of Anesthesia and Intensive Care, Hospital of Santa Croce Di Moncalieri, Turin, 10024, Italy.
Emiliano PetrucciDepartment of Anesthesia and Intensive Care Unit, San Salvatore Academic Hospitalof, L'Aquila, 67100, Italy.
Alessandro VergalloDepartment of Anesthesia and Intensive Care, Spedali Civili di Brescia, 25121 , Brescia, Italy.
Giacomo SollecchiaDepartment of Anesthesiology, Intensive Care and Pain Treatment, University of L'Aquila, L'Aquila, 67100, Italy.
Franco MarinangeliDepartment of Anesthesiology, Intensive Care and Pain Treatment, University of L'Aquila, L'Aquila, 67100, Italy.
Roberto PedoneDepartment of Psychology, University of Campania Luigi Vanvitelli, Caserta, 8100 , Italy.
Alessandro VittoriDepartment of Anesthesia, Critical Care and Pain Medicine, ARCO, Ospedale Pediatrico Bambino Gesù IRCCS, Rome, 00165, Italy. alexvittori82@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBurnout (BO) is a serious issue affecting professionals across various sectors, leading to adverse psychological and occupational consequences, even in anesthesiologists. Machine learning, particularly neural networks, can offer effective data-driven approaches to identifying BO risk more accurately. This study aims to develop and evaluate different artificial dense neural network (DNN)-based models to predict BO based on occupational, psychological, and behavioral factors.

methodsA dataset (300 Italian anesthesiologists) comprising workplace stressors, psychological well-being indicators, and demographic variables was used to train DNN models. Model performance was measured using standard evaluation metrics, including accuracy, precision, recall, and F1 score. Statistical tests were adopted to assess differences in prediction across the DNNs.

resultsThe best neural architecture achieved a predictive accuracy of 0.68, with key contributors to BO including workload, emotional exhaustion, job dissatisfaction, and lack of work-life balance. Despite substantial differences among the six implemented algorithms, no significant variation in prediction performance was observed.

conclusionPsychological distress scores are significantly higher in the high-risk BO group, suggesting greater anxiety, depression, and overall distress in this category. While challenges remain, continued advancements in artificial intelligence and data science promise more effective and personalized mental health care solutions.

trial registrationNot applicable.

Indexed as

AnesthesiaAnesthesiologistsArtificial intelligenceArtificial neural networksBurnoutCritical careMental healthPain

Identifiers

PMID40597440
PMCPMC12220590

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.