Bibliographic record
Abstract
The majority of cancer patients experience wasting characterized by muscle loss with or without fat loss. In human and animal models of cancer, body composition assessment and morphological analysis reveals adipose atrophy and presence of smaller adipocytes. Fat loss is associated with reduced quality of life in cancer patients and shorter survival independent of body mass index. Fat loss occurs in both visceral and subcutaneous depots; however, the pattern of loss has been incompletely characterized. Increased lipolysis and fat oxidation, decreased lipogenesis, impaired lipid depositionand adipogenesis, as well as browning of white adipose tissue may underlie adipose atrophy in cancer. Inflammatory cytokines such as interleukin-6 (IL-6), tumor necrosis factor alpha (TNF-α), and interleukin-1 beta (IL-1β) produced by the tumor or adipose tissue may also contribute to adipose depletion. Identifying the mechanisms and time course of fat mass changes in cancer may help identify individuals at risk of adipose depletion and define interventions to circumvent wasting. This review outlines current knowledge of fat mass in cancer and illustrates the need for further studies to assess alterations in visceral and subcutaneous adipose depots and possible mechanisms for loss of fat during cancer progression.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".