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Record W3120921976 · doi:10.3389/fonc.2020.598425

Multiphasic Prehabilitation Across the Cancer Continuum: A Narrative Review and Conceptual Framework

2021· review· en· W3120921976 on OpenAlexaff
Daniel Santa Mina, Stefanus J. van Rooijen, Enrico Maria Minnella, Shabbir M.H. Alibhai, Priya Brahmbhatt, Susanne Oksbjerg Dalton, Chelsia Gillis, Michael P. W. Grocott, Doris Howell, Ian Randall, Catherine M. Sabiston, Julie K. Silver, Gerrit D. Slooter, Malcolm West, Sandy Jack, Franco Carli

Bibliographic record

VenueFrontiers in Oncology · 2021
Typereview
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsUniversity of CalgaryMcGill University Health CentreUniversity of TorontoUniversity Health Network
FundersNational Institute for Health and Care Research
KeywordsPrehabilitationMedicineSurvivorship curveCancerPsychological interventionContinuum of careCancer recurrenceOncologyCancer treatmentCancer survivorshipPhysical therapyInternal medicineHealth careNursing

Abstract

fetched live from OpenAlex

The field of cancer survivorship has significantly advanced person-centered care throughout the cancer continuum. Within cancer survivorship, the last decade has seen remarkable growth in the investigation of prehabilitation comprising pre-treatment interventions to prevent or attenuate the burden of oncologic therapies. While the majority of evidence remains in the surgical setting, prehabilitation is being adapted to target modifiable risk factors that predict poor treatment outcomes in patients receiving other systemic and localized anti-tumor treatments. Here, we propose a multiphasic approach for prehabilitation across the cancer continuum, as a conceptual framework, to encompass the variability in cancer treatment experiences while adopting the most inclusive definition of the cancer survivor.

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)0.002
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.005
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.040
GPT teacher head0.434
Teacher spread0.394 · 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 designTheoretical or conceptual
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

Citations97
Published2021
Admission routes1
Has abstractyes

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