Evidence map›Paper›PMID 39097739›Full record

ArticleBMC palliative care2024

Advancing the integration of biosignal-based automated pain assessment methods into a comprehensive model for addressing cancer pain.

Marco Cascella, Piergiacomo Di Gennaro, Anna Crispo, Alessandro Vittori, Emiliano Petrucci, Francesco Sciorio, Franco Marinangeli, Alfonso Maria Ponsiglione, Maria Romano, Concetta Ovetta and 5 more

2 registry-linked trialsAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC palliative care, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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.

NCT04726228 recruitingnot on this map

Home-Based Telemedicine for Automatic Pain Assessment in Cancer Patients: Dataset Creation and Development of Machine Learning Algorithms

TypeobservationalSponsorNational Cancer Institute, NaplesRan2021 to 2025Enrolled40ConditionsOncology, Cancer Pain, Quality of Life
NCT07038434 narecruitingnot on this mapstarted 2025, after this paper: background citation

Refining mUltiple Artificial intelliGence strateGies for Automatic Pain Assessment Investigations: RUGGI Study

TypeinterventionalSponsorValentina CerroneRan2025 to 2026Enrolled200ConditionsChronic Pain, Cancer Pain, Neuropathic Pain, Pain AssessmentArmsMultimodal AI-Based Pain Assessment
3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Article
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  6. Review
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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

15 authors.

Marco CascellaDepartment of Medicine, Surgery and Dentistry, Anesthesia and Pain Medicine, University of Salerno, Via Salvador Allende 43, Baronissi Salerno, 84081, Italy.
Piergiacomo Di GennaroEpidemiology and Biostatistics Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, Via Mariano Semmola 53, Naples, 80131, Italy.
Anna CrispoEpidemiology and Biostatistics Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, Via Mariano Semmola 53, Naples, 80131, Italy.
Alessandro VittoriDepartment of Anesthesia and Critical Care, ARCO Roma, Ospedale Pediatrico Bambino Gesù IRCCS, Piazza S. Onofrio 4, Rome, 00165, Italy. alexvittori82@gmail.com.
Emiliano PetrucciDepartment of Anesthesia and Intensive Care Unit, San Salvatore Academic Hospital of L'Aquila, Via Lorenzo Natali, 1, Coppito L'Aquila, 67100, Italy.
Francesco SciorioDepartment of Anesthesiology, Intensive Care and Pain Treatment, University of L'Aquila, Piazzale Salvatore Tommasi, 1,, Coppito, AQ, 67100, Italy.
Franco MarinangeliDepartment of Anesthesiology, Intensive Care and Pain Treatment, University of L'Aquila, Piazzale Salvatore Tommasi, 1,, Coppito, AQ, 67100, Italy.
Alfonso Maria PonsiglioneDepartment of Information Technology and Electrical Engineering, University of Naples Federico II, Corso Umberto I, 40, Napoles, 80138, Italy.
Maria RomanoDepartment of Information Technology and Electrical Engineering, University of Naples Federico II, Corso Umberto I, 40, Napoles, 80138, Italy.
Concetta OvettaDepartment of Information Technology and Electrical Engineering, University of Naples Federico II, Corso Umberto I, 40, Napoles, 80138, Italy.
Alessandro OttaianoSSD Innovative Therapies for Abdominal Metastases, Abdominal Oncology, INT IRCCS Foundation "G. Pascale", Via Mariano Semmola 53, Naples, 80131, Italy.
Francesco SabbatinoDepartment of Medicine, Surgery and Dentistry, Oncology Unit, University of Salerno, Via Salvador Allende 43, Baronissi Salerno, 84081, Italy.
Francesco PerriMedical and Experimental Head and Neck Oncology Unit, Istituto Nazionale Tumori - IRCCS Fondazione G. Pascale, Via Mariano Semmola 53, Naples, 80131, Italy.
Ornella PiazzaDepartment of Medicine, Surgery and Dentistry, Anesthesia and Pain Medicine, University of Salerno, Via Salvador Allende 43, Baronissi Salerno, 84081, Italy.
Sergio ColucciaEpidemiology and Biostatistics Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, Via Mariano Semmola 53, Naples, 80131, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTailoring effective strategies for cancer pain management requires a careful analysis of multiple factors that influence pain phenomena and, ultimately, guide the therapy. While there is a wealth of research on automatic pain assessment (APA), its integration with clinical data remains inadequately explored. This study aimed to address the potential correlations between subjective and APA-derived objectives variables in a cohort of cancer patients.

methodsA multidimensional statistical approach was employed. Demographic, clinical, and pain-related variables were examined. Objective measures included electrodermal activity (EDA) and electrocardiogram (ECG) signals. Sensitivity analysis, multiple factorial analysis (MFA), hierarchical clustering on principal components (HCPC), and multivariable regression were used for data analysis.

resultsThe study analyzed data from 64 cancer patients. MFA revealed correlations between pain intensity, type, Eastern Cooperative Oncology Group Performance status (ECOG), opioids, and metastases. Clustering identified three distinct patient groups based on pain characteristics, treatments, and ECOG. Multivariable regression analysis showed associations between pain intensity, ECOG, type of breakthrough cancer pain, and opioid dosages. The analyses failed to find a correlation between subjective and objective pain variables.

conclusionsThe reported pain perception is unrelated to the objective variables of APA. An in-depth investigation of APA is required to understand the variables to be studied, the operational modalities, and above all, strategies for appropriate integration with data obtained from self-reporting.

trial registrationThis study is registered with ClinicalTrials.gov, number (NCT04726228), registered 27 January 2021, https://classic. CLINICALTRIALS: gov/ct2/show/NCT04726228?term=nct04726228&draw=2&rank=1.

Indexed as

Cancer PainPain MeasurementAdultAgedAged, 80 and overCohort StudiesElectrocardiographyFemaleGalvanic Skin ResponseHumansMaleMiddle AgedPain ManagementArtificial IntelligenceAutomatic Pain AssessmentBreakthrough Cancer PainCancer PainOncologyOpioidsPainPain ManagementPalliative CareQuality of Life

Identifiers

PMID39097739
PMCPMC11297625

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

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.