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Record W4321749297 · doi:10.3322/caac.21773

Translating energy balance research from the bench to the clinic to the community: Parallel animal‐human studies in cancer

2023· review· en· W4321749297 on OpenAlex

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

VenueCA A Cancer Journal for Clinicians · 2023
Typereview
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsUniversity of Alberta
FundersUniversity of Texas MD Anderson Cancer CenterAmerican Gastroenterological AssociationMelanoma Research AlliancePfizerNational Cancer InstituteConquer Cancer FoundationBristol-Myers SquibbAmerican Association for Cancer Research
KeywordsCancerEnergy balanceBalance (ability)Translational researchPopulationEnergy (signal processing)MedicineGerontologyPhysical therapyEnvironmental healthBiologyPathologyInternal medicine

Abstract

fetched live from OpenAlex

Advances in energy balance and cancer research to date have largely occurred in siloed work in rodents or patients. However, substantial benefit can be derived from parallel studies in which animal models inform the design of clinical and population studies or in which clinical observations become the basis for animal studies. The conference Translating Energy Balance from Bench to Communities: Application of Parallel Animal-Human Studies in Cancer, held in July 2021, convened investigators from basic, translational/clinical, and population science research to share knowledge, examples of successful parallel studies, and strong research to move the field of energy balance and cancer toward practice changes. This review summarizes key topics discussed to advance research on the role of energy balance, including physical activity, body composition, and dietary intake, on cancer development, cancer outcomes, and healthy survivorship.

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)0.022
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.514
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0220.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0000.002
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0030.001
Research integrity0.0000.007
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 head0.690
GPT teacher head0.631
Teacher spread0.059 · 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