Adipose Tissue Radiodensity in Chronic Diseases: A Literature Review of the Applied Methodologies
Bibliographic record
Abstract
Abstract Background: The concept of adipose tissue radiodensity is emerging and its relationship to disease prognosis has been infrequently explored. The aims of the present study were to evaluate published literature that explored adipose tissue radiodensity in relation to outcomes in health and disease and to summarize methodologies used to evaluate adipose tissue radiodensity by computed tomography (CT). Methods: A comprehensive literature review included all published studies that applied CT imaging of the abdominal region to define adipose tissue radiodensity. The review was performed without regard for study design or quality. Results: We identified 22 studies that evaluated the relationship between adipose tissue radiodensity and outcomes. The literature reviewed highlights significant methodological variation in terms of abdominal region selected, slice thickness, contrast media, dose, software, and radiodensity ranges used to define adipose tissues. This is primarily due to a lack of consensus about the effect such methodological variables have on body composition parameters. Conclusions: Authors should carefully report adipose tissue radiodensity, especially when it comes to prognosis inference. Consensus on methodology will enable meaningful advancement in understanding the importance of adipose tissue radiodensity in different disease conditions.
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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".