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Record W3134319976 · doi:10.3389/fnut.2021.644723

Current Landscape of Nutrition Within Prehabilitation Oncology Research: A Scoping Review

2021· review· en· W3134319976 on OpenAlex
Chelsia Gillis, Sarah Davies, Francesco Carli, Paul E. Wischmeyer, Stephen A. Wootton, Alan A. Jackson, Bernhard Riedel, Luise V. Marino, Denny Levett, Malcolm West

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFrontiers in Nutrition · 2021
Typereview
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsMcGill University
FundersNational Institute for Health and Care Research
KeywordsPrehabilitationCINAHLPsychological interventionMedicinePsychosocialMEDLINEIntervention (counseling)Randomized controlled trialPhysical therapyNursingInternal medicine

Abstract

fetched live from OpenAlex

Background: Prehabilitation aims to improve functional capacity prior to cancer treatment to achieve better psychosocial and clinical outcomes. Prehabilitation interventions vary considerably in design and delivery. In order to identify gaps in knowledge and facilitate the design of future studies, we undertook a scoping review of prehabilitation studies to map the range of work on prehabilitation being carried out in any cancer type and with a particular focus on diet or nutrition interventions. Objectives: Firstly, to describe the type of prehabilitation programs currently being conducted. Secondly, to describe the extent to which prehabilitation studies involved aspects of nutrition, including assessment, interventions, implementation, and outcomes. Eligibility Criteria: Any study of quantitative or qualitative design that employed a formal prehabilitation program before cancer treatment (“prehabilitation” listed in keywords, title, or abstract). Sources of Evidence: Search was conducted in July 2020 using MEDLINE, PubMed, EMBASE, EMCARE, CINAHL, and AMED. Charting Methods: Quantitative data were reported as frequencies. Qualitative nutrition data were charted using a framework analysis that reflects the Nutrition Care Process Model: assessment, intervention, and monitoring/evaluation of the nutrition intervention. Results: Five hundred fifty unique articles were identified: 110 studies met inclusion criteria of a formal prehabilitation study in oncology. prehabilitation studies were mostly cohort studies (41%) or randomized-controlled trials (38%) of multimodal (49%), or exercise-only (44%) interventions that were applied before surgery (94%). Nutrition assessment was inconsistently applied across these studies, and often conducted without validated tools (46%). Of the 110 studies, 37 (34%) included a nutrition treatment component. Half of these studies provided the goal for the nutrition component of their prehabilitation program; of these goals, less than half referenced accepted nutrition guidelines in surgery or oncology. Nutrition interventions largely consisted of counseling with dietary supplementation. The nutrition intervention was indiscernible in 24% of studies. Two-thirds of studies did not monitor the nutrition intervention nor evaluate nutrition outcomes. Conclusion: Prehabilitation literature lacks standardized and validated nutritional assessment, is frequently conducted without evidence-based nutrition interventions, and is typically implemented without monitoring the nutrition intervention or evaluating the intervention's contribution to outcomes. We suggest that the development of a core outcome set could improve the quality of the studies, enable pooling of evidence, and address some of the research gaps 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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.387
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.242
GPT teacher head0.536
Teacher spread0.293 · 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