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Record W1976201942 · doi:10.1038/bjc.2012.554

Implementing improved post-treatment care for cancer survivors in England, with reflections from Australia, Canada and the USA

2012· review· en· W1976201942 on OpenAlexaffabout
Michael Jefford, Julia H. Rowland, Eva Grunfeld, Marcus Richards, JaneMaree Maher, Adam Glaser

Bibliographic record

VenueBritish Journal of Cancer · 2012
Typereview
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsUniversity of TorontoOntario Institute for Cancer Research
Fundersnot available
KeywordsSurvivorship curveCancer survivorshipMedicineCancerHealth careMEDLINEFamily medicineGerontologyNursingEconomic growthPolitical science

Abstract

fetched live from OpenAlex

Increasing cancer incidence together with improved survival rates are contributing to the growing number of cancer survivors. Survivors may encounter a range of potential effects as a result of the cancer itself or cancer treatments. Traditionally, the major focus of follow-up care has been on detection of cancer recurrence; however, the efficacy of such strategies is questionable. Traditional follow-up frequently fails to identify or adequately address many survivors' concerns. Aftercare needs to be planned to enable better outcomes for survivors, while using scarce health-care resources efficiently. This review focuses on provision of survivorship care, rather than on research. England's National Cancer Survivorship Initiative has developed principles for improved care of those living with and beyond cancer. These include risk-stratified pathways of care, the use of treatment summaries and care plans, information and education to enable choice and the confidence to self manage, rapid re-access to specialist care, remote monitoring and well-coordinated care. Many of these principles are relevant internationally, though preferred models of care will depend on local circumstances.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Review
About the Canadian research system: no · About a Canadian topic: yes
Not applicablelow
gptno category
Domain: not available · Genre: Review
About the Canadian research system: no · About a Canadian topic: yes
Other designlow
models splitAgreement compares identical category sets and study designs across arms.

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 score0.625
Threshold uncertainty score0.745

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.004
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.054
GPT teacher head0.369
Teacher spread0.316 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designNot applicable · Other design
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

Citations159
Published2012
Admission routes2
Has abstractyes

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