MétaCan
Menu
Back to cohort
Record W2885622888 · doi:10.1097/spc.0000000000000379

How much does reduced food intake contribute to cancer-associated weight loss?

2018· review· en· W2885622888 on OpenAlexaff
Lisa Martin, Catherine Kubrak

Bibliographic record

VenueCurrent Opinion in Supportive and Palliative Care · 2018
Typereview
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsWeight lossMedicineCachexiaCancer cachexiaFood intakeCancerObesityPhysiologyEndocrinologyInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: An international consensus group defined cancer cachexia as a syndrome of involuntary weight loss, characterized by loss of skeletal muscle (with or without fat loss), which is driven by a variable combination of reduced food intake and altered metabolism.This review presents recent studies that evaluated the contribution of reduced food intake to cancer-associated weight loss. RECENT FINDINGS: Four studies examined food intake in relation to weight loss. Heterogeneity among studies rendered aggregation and interpretation of results challenging. Despite these limitations, reduced food intake had consistent significant, independent associations with weight loss. However, reduced food intake did not explain all the variation in weight loss; and limited data suggests factors related to alterations in metabolism (e.g. increased resting energy expenditure, systemic inflammation) are also contributing to weight loss. SUMMARY: Reduced food intake is a significant contributor to cancer-associated weight loss. Understanding the magnitude of the association between food intake and weight loss may improve when it is possible to account for alterations in metabolism. Efforts to align clinical assessments of food intake to reduce heterogeneity are needed.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.836
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.197
GPT teacher head0.470
Teacher spread0.274 · 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.

Study designNot applicable
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

Citations29
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueCurrent Opinion in Supportive and Palliative CareSame topicNutrition and Health in AgingFrench-language works237,207