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Record W3167108263 · doi:10.1097/mpa.0000000000001829

Diet and Exercise Interventions in Patients With Pancreatic Cancer

2021· article· en· W3167108263 on OpenAlexaff
Popi Kasvis, Robert D. Kilgour

Bibliographic record

VenuePancreas · 2021
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsEspace pour la vieConcordia UniversityMcGill University Health Centre
Fundersnot available
KeywordsMedicinePsychological interventionWastingPancreatic cancerAmbulatoryPhysical therapyRandomized controlled trialMEDLINEMalnutritionCancerGerontologyInternal medicineNursing

Abstract

fetched live from OpenAlex

ABSTRACT: Diet and exercise interventions may help reverse malnutrition and muscle wasting common in pancreatic cancer. We performed a scoping review to identify the knowledge gaps surrounding diet and exercise interventions. We searched PubMed, Scopus, Cumulative Index to Nursing and Allied Health Literature, Embase, ProQuest Theses and Dissertations, and Google Scholar using the umbrella terms of "pancreatic cancer," "diet/nutrition," and "exercise." Included were articles reporting on ambulatory adults with diagnosed pancreatic cancer. Excluded were studies examining prevention and/or risk, animal, or cell lines. Of the 15,708 articles identified, only 62 met the final inclusion criteria. Almost half of the articles were randomized controlled studies (n = 27). Most studies were from the United States (n = 20). The majority examined dietary interventions (n = 41), with 20 assessing the use of omega-3 fatty acids. Exercise interventions were reported in 13 studies, with 8 examining a diet and exercise intervention. Most studies were small and varied greatly in terms of study design, intervention, and outcomes. We identified 7 research gaps that should be addressed in future studies. This scoping review highlights the limited research examining the effect of diet and exercise interventions in ambulatory patients with pancreatic 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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.346

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.032
GPT teacher head0.329
Teacher spread0.297 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations10
Published2021
Admission routes1
Has abstractyes

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