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Record W4363645875 · doi:10.1080/01635581.2023.2195543

Time-Restricted Eating in Breast Cancer Survivors: Effects on Body Composition and Nutritional Status

2023· article· en· W4363645875 on OpenAlexafffund
Bruna Ramos da Silva, Amy A. Kirkham, Katherine L. Ford, Mark J. Haykowsky, D. Ian Paterson, Anil A. Joy, Edith Pituskin, Richard B. Thompson, Carla M. Prado

Bibliographic record

VenueNutrition and Cancer · 2023
Typearticle
Languageen
FieldMedicine
TopicDietary Effects on Health
Canadian institutionsUniversity of TorontoUniversity of Alberta
FundersCanadian Institutes of Health ResearchHeart and Stroke Foundation of Canada
KeywordsBioelectrical impedance analysisMedicineBody mass indexBreast cancerBody waterObesityInternal medicinePhase angle (astronomy)AnthropometryComposition (language)Body weightCancerPhysiology

Abstract

fetched live from OpenAlex

In this secondary analysis of an 8-wk single-arm feasibility study of weekday time-restricted eating (TRE), we explored the effects of TRE on body composition. Women (n = 22; ≥60 yr) who had completed chemotherapy for early-stage breast cancer and had a body mass index ≥25 kg/m2 were enrolled. Bioelectrical impedance analysis was performed before and after 8 wk of TRE, and nutritional status was evaluated by bioelectrical impedance vector analysis (BIVA). Body weight (p = 0.01) and total fat mass (p = 0.04) decreased with TRE. Phase angle was low (defined as ≤5.6°) in 86% of participants at baseline and did not change. Four participants who initially presented with obesity (>95% ellipse, BIVA) had favorable body composition modifications after TRE. Our study highlighted a less favorable body composition profile, poorer cell integrity and overhydration in these patients. BIVA was a useful method to assess body composition and hydration. A short TRE intervention was associated with decreased estimated fat mass and a favorable change in nutritional status in those with obesity.

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 score0.330
Threshold uncertainty score0.517

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 head0.012
GPT teacher head0.305
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

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

Citations3
Published2023
Admission routes2
Has abstractyes

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