Evidence map›Paper›PMID 42451470›Full record

ArticleSensors (Basel, Switzerland)2026

Physiological Data Analysis Framework for Pain Prediction in Physical Rehabilitation.

Abdel Hiram Cital Duarte, Gilberto Borrego, Samuel González-López, Erica Cecilia Ruiz Ibarra

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Abdel Hiram Cital DuarteDepartment of Electrical and Electronics, Instituto Tecnológico de Sonora, Ciudad Obregón 85130, Sonora, Mexico.ORCID 0009-0001-4105-4289
Gilberto BorregoDepartment of Computing and Design, Instituto Tecnológico de Sonora, Ciudad Obregón 85130, Sonora, Mexico.ORCID 0000-0001-7315-5693
Samuel González-LópezDepartment of Mathematics, Universidad de Sonora, Hermosillo 83000, Sonora, Mexico.ORCID 0000-0002-1511-1227
Erica Cecilia Ruiz IbarraDepartment of Electrical and Electronics, Instituto Tecnológico de Sonora, Ciudad Obregón 85130, Sonora, Mexico.ORCID 0000-0002-7020-4960

Funding

Programa de Fomento y Apoyo a Proyectos de Investigación (PROFAPI) 2026_074Secretaría de Ciencia, Humanidades, Tecnología e Innovación (SECIHTI) 1090799
6 · The paper itself

Abstract

Predicting pain in physical rehabilitation is challenging due to subjectivity, patient variability, and self-report bias, especially in telerehabilitation. This study aims to determine whether machine-learning models based on heart rate (HR), heart rate variability (HRV), and peripheral oxygen saturation (SpO

Indexed as

PainAdultFemaleHeart RateHumansMachine LearningMaleMiddle AgedNeural Networks, ComputerOximetryOxygen SaturationPrediction AlgorithmsPredictive Learning ModelsRandom ForestYoung Adultbiomedical signal processingheart rateheart rate variabilitymachine learningoxygen saturationpain predictionphysical rehabilitationtelerehabilitation

Identifiers

PMID42451470
PMCPMC13363754

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.