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Record W2084027094 · doi:10.1097/spc.0000000000000086

Immunomodulatory agents for the treatment of cachexia

2014· review· en· W2084027094 on OpenAlexaff
Martin Chasen, Ravi Bhargava, Shalom Z. Hirschman

Bibliographic record

VenueCurrent Opinion in Supportive and Palliative Care · 2014
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsBruyèreÉlisabeth Bruyère Hospital
FundersUniversity of Texas MD Anderson Cancer Center
KeywordsMedicineCachexiaIntensive care medicineMEDLINEInternal medicineCancerBiochemistryBiology

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: In patients with advanced cancer, AIDS and end-stage organ diseases, symptoms of cachexia syndrome include decrease in appetite, weight loss, decreased performance status, and an increase in the systemic inflammatory response. Inflammatory cytokines and other immune interactions affect the lean tissue mass and body fat. It is hopeful that modulation of these inflammatory interactions may contribute to the delay in the development and treatment of cachexia. This review summarizes the current state of the art. RECENT FINDINGS: This article covers the role of inflammatory response in cachexia, measurement of inflammatory response, mechanism and measurement of cachexia, immunomodulation in cancer, drugs targeting inflammatory cytokines, effect of exercise in cachexia, and treatment of cancer cachexia using immunomodulatory agents. SUMMARY: Understanding the immune response associated with cachexia may improve future pharmacological modification of the cytokines. In addition, the multifactorial contributions to the mechanisms of cachexia indicate that a multimodal approach may be necessary to treat cachexia and its associated symptoms.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.994
Threshold uncertainty score0.715

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.133
GPT teacher head0.439
Teacher spread0.306 · 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 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

Citations10
Published2014
Admission routes1
Has abstractyes

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