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Record W2153316603 · doi:10.1177/1534735409355293

Circadian Disruption, Fatigue, and Anorexia Clustering in Advanced Cancer Patients: Implications for Innovative Therapeutic Approaches

2009· review· en· W2153316603 on OpenAlexaff
Pasquale F. Innominato, Marie‐Christine Mormont, Tyvin A. Rich, Jim Waterhouse, Françis Lévi, Georg A. Bjarnason

Bibliographic record

VenueIntegrative Cancer Therapies · 2009
Typereview
Languageen
FieldNeuroscience
TopicCircadian rhythm and melatonin
Canadian institutionsSunnybrook Health Science CentrePfizer (Canada)
Fundersnot available
KeywordsCircadian rhythmAnorexiaMedicineCircadian clockCancer-related fatigueCancerInternal medicine

Abstract

fetched live from OpenAlex

A disruption of the circadian timing system, as identified by monitoring of marker biorhythms, is common in cancer patients. The recording of the rest-activity rhythm with a wrist actigraph has been commonly used. This noninvasive monitoring allows a robust estimation of circadian disruption. The authors have previously found that altered patterns of circadian rest-activity rhythms are significantly and independently associated with the severity of fatigue and anorexia in patients with metastatic colorectal cancer. Elevated proinflammatory cytokines could partly account for this circadian disruption and its associated constitutional symptoms. Here, the authors present and discuss the data supporting the hypothesis that circadian disruption is often associated with fatigue and anorexia, which in turn further alter and dampen circadian synchronization, thus, creating a vicious cycle. This body of evidence paves the path for innovative therapeutic approaches targeting the circadian timing system in an effort to diminish constitutional symptoms induced by cancer and some anticancer treatments.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.158
GPT teacher head0.390
Teacher spread0.232 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations48
Published2009
Admission routes1
Has abstractyes

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