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Record W2596672650 · doi:10.15173/m.v1i25.851

L-Carnitine Supplementation: A Potential Treatment for Cancer Cachexia

2014· article· en· W2596672650 on OpenAlexaffvenue
Aaron Kwong, Obaidullah Khan, Katrina Fleming, Sina Moshiri, Muhammad Laeeq ur Rehman Hashmi

Bibliographic record

VenueThe Meducator · 2014
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsMcMaster University
Fundersnot available
KeywordsCachexiaMedicineCarnitineCancer cachexiaCancerPeer reviewCancer treatmentOncologyInternal medicineBiology

Abstract

fetched live from OpenAlex

Cachexia is a paraneoplastic syndrome that exhibits rampant muscle wasting, unintentional weight loss, fatigue, weakness, and overall loss of appetite, none of which can be abated by an increase in caloric intake. Metabolic derailment by cachexia is so severe in cancer patients that it can shorten lifetime expectancy and lead to death before the course of treatment is finished. Etiology may involve proinflammatory cytokines, but the abnormal loss of muscle, protein, and fat suggests an underlying metabolic dysfunction that contributes to cachexia. Previous research has shown reduction of L-carnitine in cachectic patients and chemotherapy-induced damage to the L-carnitine transport system that may further exacerbate symptoms. As such, a possible avenue of treatment for cachexia-induced fat loss may involve L-carnitine supplementation to restore metabolic homeostasis through various mechanisms such as lowering levels of proinflammatory cytokines and restoring L-carnitine palmitoyltransferase activity. While the precise mechanism of L-carnitine=mediated amelioration has not been determined, research findings support the notion of L-carnitine as an alleviator of several cachectic symptoms that have previously been unmanageable in a clinical setting.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.541
Threshold uncertainty score1.000

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)0.0010.000

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 head0.067
GPT teacher head0.417
Teacher spread0.350 · 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
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

Citations0
Published2014
Admission routes2
Has abstractyes

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