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Record W3163503919 · doi:10.1002/pbc.29090

Complex behavioral interventions targeting physical activity and dietary behaviors in pediatric oncology: A scoping review

2021· review· en· W3163503919 on OpenAlexaff
Catherine Demers, Annie Brochu, Johanne Higgins, Isabelle Gélinas

Bibliographic record

VenuePediatric Blood & Cancer · 2021
Typereview
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsUniversité de MontréalCentre for Interdisciplinary Research in RehabilitationMcGill UniversityCentre Hospitalier Universitaire Sainte-JustineJewish Rehabilitation Hospital
Fundersnot available
KeywordsMedicinePsychological interventionModalitiesPediatric cancerQuality of life (healthcare)Physical activityPediatric oncologyAdverse effectMEDLINECancerPhysical therapyPsychiatryInternal medicineNursing

Abstract

fetched live from OpenAlex

As cancer and its treatment negatively impacts the long-term health and quality of life of survivors, there is a need to explore new avenues to prevent or minimize the impact of adverse effects in children with cancer and cancer survivors. Therefore, this scoping review aimed to report on the state of the evidence on the use and effects of complex behavioral interventions (CBI) targeting physical activity and/or dietary behaviors in pediatric oncology. Fourteen quantitative studies were included, evaluating interventions that used a combination of two or three different treatment modalities. Overall, studies demonstrated that it is feasible to implement CBI and that they can potentially improve physical activity and dietary behaviors as well as patient outcomes such as physical and psychological health. Unfortunately, due to a paucity of studies and the heterogeneity of the studies included in this review, no conclusive evidence favoring specific interventions were identified.

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)0.005
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.018
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.005
Bibliometrics0.0070.007
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.191
GPT teacher head0.499
Teacher spread0.308 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations6
Published2021
Admission routes1
Has abstractyes

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