Evidence map›Paper›PMID 42302141›Full record

ArticleJournal of evaluation in clinical practice2026

Evaluation of Pain in Infants of the First 100 Days of Age With Artificial Intelligence Techniques.

Ayfer Acikgoz, Deniz Yigit, Ozer Celik

Abstract read
In one paragraph

Article in Journal of evaluation in clinical practice, 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

3 authors.

Ayfer AcikgozDepartment of Child Health and Diseases Nursing, Faculty of Health Sciences, Eskisehir Osmangazi University, Eskişehir, Turkey.ORCID https://orcid.org/0000-0002-3803-9678
Deniz YigitDepartment of Child Health and Diseases Nursing, Faculty of Health Sciences, Kütahya Health Sciences University, Kütahya, Turkey.ORCID https://orcid.org/0000-0001-5627-7963
Ozer CelikArtificial Intelligence Application and Research Center, Anadolu University, Eskişehir, Turkey.ORCID https://orcid.org/0000-0002-4409-3101

Funding

TÜBİTAK 1005-National New Ideas and Products Research Support Programme 221S569
6 · The paper itself

Abstract

rationalePain assessment in infants is difficult due to the lack of verbal communication and the subjective nature of existing methods. Current tools require evaluating multiple indicators separately, which can be time-consuming and variable between observers. Artificial intelligence can enable rapid and standardised direct pain scoring, improving accuracy and clinical efficiency. The limited number of comprehensive studies in this area highlights an important gap in the literature. AIMS AND

objectivesThis research was conducted to develop an application that will evaluate pain in infants in the first 100 days of life using artificial intelligence techniques.

methodsThis study is an artificial intelligence-based research conducted with 1000 newborns hospitalised in a Neonatal Intensive Care Unit. A data collection form and the Neonatal Pain, Agitation, and Sedation Scale were used as data collection tools. Data analysis was performed using IBM SPSS Statistics 21.0, with descriptive statistics and Cohen's Kappa test applied. A p value of < 0.05 was considered statistically significant. Image and audio recordings from all 1000 newborns were independently labelled by the researchers, and these labelled data were used to develop the artificial intelligence model. The labelled image and audio recordings were utilised for model training (80%), validation (10%), and testing (10%).

resultsThe mean gestational age of infants was determined to be 38.52 ± 1.07 weeks, and the postnatal age was determined to be 2.90 ± 0.77 days. It was determined that there was no difference between the pain scale scores labelled by the researchers (p > 0.05). It was determined that the success rate of the created artificial intelligence model in correctly predicting the presence of pain in newborns was 82%.

conclusionsIt was determined that the developed artificial intelligence model was successful in predicting the presence of pain in newborns in the Neonatal Intensive Care Unit.

Indexed as

Artificial IntelligencePain MeasurementFemaleHumansInfant, NewbornIntensive Care Units, NeonatalMaleartificial intelligenceneonatal intensive care unitnewbornN‐PASSnursepain

Identifiers

PMID42302141
PMCPMC13271532

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.