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Hypercatabolism and hypermetabolism in wasting states

2002· editorial· en· W2060641274 on OpenAlexaff
Vickie E. Baracos

Bibliographic record

VenueCurrent Opinion in Clinical Nutrition & Metabolic Care · 2002
Typeeditorial
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsHypermetabolismWastingCachexiaWasting SyndromeAnabolismAnorexiaMedicineWeight lossBioinformaticsIntensive care medicineDiseaseMuscle atrophyLean body massEndocrinologySkeletal muscleInternal medicineCancerBiologyObesityBody weight

Abstract

fetched live from OpenAlex

The year 2001/2002 has been marked by a number of exciting new results for our understanding of anabolic and catabolic mediators and their participation in wasting states, as reflected by the contents of this section. It becomes ever more apparent that a clear understanding of how to shut off hypercatabolic and hypermetabolic processes is needed to underpin effective strategies for wasting syndromes. A particularly interesting development in the control of degradative processes in skeletal muscle is the discovery of several muscle-specific ubiquitin ligases. These enzymes, which confer specificity to the degradation of myofibrillar proteins and are situated in a pathway of proteolysis common to a variety of wasting states, may prove to be a valuable point of intervention in muscle atrophy. In the clinical arena, studies on non-small cell lung cancer patients as well as broader patient populations with solid tumours provide more evidence for a high incidence of hypermetabolism as well as low energy intake. The best therapies currently available for the cancer cachexia/anorexia syndrome have numerous limitations and tend mainly to attenuate losses rather than to promote a net gain of weight or lean body mass. Sustained hypermetabolism over the long course of disease progression constitutes an important contributor to negative energy balance, and its presence is likely to be a limiting factor to the success of current treatment approaches.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.184
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.039
GPT teacher head0.383
Teacher spread0.344 · 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
GenreEditorial

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

Citations36
Published2002
Admission routes1
Has abstractyes

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