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Record W4409244833 · doi:10.1002/cam4.70867

Navigating Life With High‐Grade Glioma: Experiences and Needs of Adolescents and Young Adults

2025· article· en· W4409244833 on OpenAlexafffund
Kaviya Devaraja, Maureen Daniels, Derek S. Tsang, Kim Edelstein, Julie Bennett, Cheryl Kanter, Warren Mason, Abha A. Gupta, Jonathan Avery

Bibliographic record

VenueCancer Medicine · 2025
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsUniversity of British ColumbiaUniversity Health NetworkHospital for Sick ChildrenPrincess Margaret Cancer CentreUniversity of Toronto
FundersOntario Ministry of Health and Long-Term CarePrincess Margaret Cancer Foundation
KeywordsThematic analysisAutonomyPsychologyPsychological interventionTriangulationCognitionYoung adultGerontologyMedicineClinical psychologyQualitative researchNursingPsychiatrySociology

Abstract

fetched live from OpenAlex

BACKGROUND: Adolescents and young adults (AYA, 18-39) with high-grade glioma (HGG) face unique challenges at a life stage focused on autonomy, careers, relationships, and family planning. AIM: This study explores their experiences to inform life-stage appropriate support and resources. METHODS: In this mixed-methods study, we surveyed AYA HGG patients at Princess Margaret Cancer Centre (PM) to assess symptom experiences and care satisfaction. Interviews further explored their illness experiences and needs. Descriptive statistics summarized survey data, and thematic analysis guided by Braun and Clarke's framework identified key interview themes. Triangulation compared survey and interview results for a comprehensive understanding. RESULTS: Seventeen participants (7 men, 10 women; mean age 30.57) completed surveys and interviews. Triangulation revealed typical AYA challenges, such as delays in education, careers, and relationships, along with HGG-specific issues. Three main themes emerged: (1) managing cognitive and treatment-related impacts on life goals, (2) addressing physical and cognitive impairments affecting relationships, and (3) navigating identity loss and independence due to neurological symptoms. CONCLUSIONS: These findings highlight the need for tailored interventions and educational support integrated into AYA HGG care pathways.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.170
Threshold uncertainty score0.806

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.015
GPT teacher head0.321
Teacher spread0.306 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations2
Published2025
Admission routes2
Has abstractyes

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