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Record W2263728449 · doi:10.1002/cncr.29834

Reimagining care for adolescent and young adult cancer programs: Moving with the times

2016· review· en· W2263728449 on OpenAlexaff
Abha A. Gupta, Janet Papadakos, Jennifer M. Jones, Leila Amin, Chana Korenblum, Daniel Santa Mina, Lianne McCabe, Laura E. Mitchell, Meredith Giuliani

Bibliographic record

VenueCancer · 2016
Typereview
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsPrincess Margaret Cancer CentreHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsMedicineCancerGerontologyPediatricsInternal medicine

Abstract

fetched live from OpenAlex

Literature regarding the development of adolescent and young adult (AYA) cancer programs has been dominantly informed by pediatric centers and practitioners. However, the majority of young adults are seen and treated at adult cancer centers, in which cancer volumes afford the development of innovative supportive care services. Although the supportive care services in adult cancer centers are helpful to AYAs, some of the most prominent and distinct issues faced by AYAs are not adequately addressed through these services alone. This article describes how the AYA Program at Princess Margaret Cancer Centre has collaborated with existing supportive care services in addition to supplying its own unique services to meet the comprehensive needs of AYAs in the domains of: symptom management (sexuality and fatigue), behavior modification (return to work and exercise), and health services (advanced cancer and survivorship). These collaborations are augmented by patient education interventions and timely referrals. The objective of this article was to assist other centers in expanding existing services to address the needs of AYA patients with cancer.

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)0.003
metaresearch head score (Gemma)0.006
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: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.045
GPT teacher head0.375
Teacher spread0.330 · 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
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

Citations83
Published2016
Admission routes1
Has abstractyes

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