Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.013 | 0.023 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".