MétaCan
Menu
← Back to cohort
Record W4407823986 · doi:10.1002/9781394191369.ch3.4

Cancer Survivorship and Rehabilitation

2025· other· en· W4407823986 on OpenAlexaff
Jiaoyang Cai, Melissa M. Hudson, Jennifer M. Jones, Nickhill Bhakta

Bibliographic record

Venuenot available
Typeother
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsPrincess Margaret Cancer CentrePublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsSurvivorship curveRehabilitationCancer survivorshipPsychologyPhysical medicine and rehabilitationGerontologyCancerMedicinePhysical therapyInternal medicine

Abstract

fetched live from OpenAlex

Advancements in cancer detection and treatment have reduced mortality in high-income countries, yet survivors face acute, persistent, and late-onset health issues. Survivorship care including rehabilitation services that address physical, emotional, and social needs is crucial to optimize the quality of survival. Despite international investments in cancer therapeutics, disparities in access to such survivorship and rehabilitation services are prevalent, especially in low- and middle-income countries. Controversies include individualized versus generic approaches and the timing of rehabilitation. Implementing existing evidence-based interventions equitably across healthcare systems and investing in innovative research, including digital health technologies, are crucial. Building capacity to overcome disparities in resources and infrastructure is also needed to ensure global access to high-quality survivor care.

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)0.001
metaresearch head score (Gemma)0.007
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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.063
Threshold uncertainty score0.210

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0630.008

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 head0.010
GPT teacher head0.293
Teacher spread0.283 · 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
GenreOther

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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same topicCancer survivorship and care→French-language works237,207→