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Record W4409885664 · doi:10.2196/66614

Pain Assessment Tools for Infants, Children, and Adolescents With Cancer: Protocol for a Scoping Review

2025· review· en· W4409885664 on OpenAlexvenueno aff
Mika Hirata, Noyuri Yamaji, Shotaro Iwamoto, Ayaka Hasegawa, Mitsuru Miyachi, Takashi Yamaguchi, Daisuke Hasegawa, Erika Ota, Nobuyuki Yotani

Bibliographic record

VenueJMIR Research Protocols · 2025
Typereview
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsCINAHLContext (archaeology)Protocol (science)MedicinePain assessmentSystematic reviewMEDLINEPopulationCochrane LibraryAlternative medicinePsychological interventionNursingPathology

Abstract

fetched live from OpenAlex

BACKGROUND: Pain management in children with cancer may be inadequate due to poor pain assessment, and evaluation using suitable tools is necessary. Despite the availability of many pain assessment scales, few studies have summarized the existing assessment tools, making it challenging to select a suitable scale. OBJECTIVE: This scoping review aims to map existing pain assessment tools for children with cancer and provide a comprehensive overview of pediatric cancer-related pain screening and assessment tools. METHODS: The scoping review will be conducted according to the guidelines by the Joanna Briggs Institute and reported following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) framework. Electronic databases, including PubMed, CINAHL, CENTRAL, ICHUSHI (Japan Medical Abstracts Society), and Embase, will be searched to identify eligible studies, without date or language restrictions. We defined the eligibility criteria based on the PCC (Population, Concept, and Context) format. Studies that focused on assessment tools for evaluating pain in children (aged 0-18 years) with cancer in a hospital or at home will be included. Although there are no restrictions on study design, protocols and conference abstracts will be excluded. Two or more reviewers will select studies by reviewing the full text of relevant articles identified by titles and abstracts, and disagreements will be resolved through discussion. Two or more reviewers will extract predefined data items, including characteristics of included studies (eg, author name, title of publication, year of publication, purpose of study, study setting, study population, outline of the assessment tool, study design, and findings) and the characteristics of assessment tools (eg, types of tools, target population, assessor, validity, instructions, precautions, and advantages and disadvantages of the tools). Pain assessment tools will be summarized in tabular format and described in a narrative synthesis. RESULTS: Through electronic database searches on November 20, 2023, we identified 3748 articles. This review will provide a comprehensive overview of pain assessment tools. The final report is planned for submission to a peer-reviewed journal in 2025. CONCLUSIONS: This scoping review is the first comprehensive effort to map existing tools on pediatric cancer-related pain assessment tools for infants, children, and adolescents aged <18 years, according to developmental stages. Based on the findings of this study, we will discuss future clinical and research implications for pain assessment and management in children with cancer. The findings are expected to enhance pain management practices in children with cancer and inform health care providers, policy makers, and other stakeholders. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/66614.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.079
metaresearch head score (Gemma)0.076
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.079
Threshold uncertainty score0.415

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0790.076
Meta-epidemiology (narrow)0.0050.005
Meta-epidemiology (broad)0.0110.017
Bibliometrics0.0170.014
Science and technology studies0.0050.004
Scholarly communication0.0070.008
Open science0.0060.007
Research integrity0.0070.007
Insufficient payload (model declined to judge)0.0730.012

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.289
GPT teacher head0.637
Teacher spread0.347 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreProtocol

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2025
Admission routes1
Has abstractyes

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