Obesity and cardiovascular disease: friend or foe?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Obesity is currently one of the greatest public health issues worldwide. However, despite its known deleterious effects on the cardiovascular system and its association with numerous cardiovascular diseases (CVD), recent findings leading to the development of concepts such as metabolically healthy obesity, the obesity paradox, and protective subcutaneous fat depots have raised a lively debate on the disparate effects of obesity on health outcomes. Regarding the concept of metabolically healthy obesity, by presumably labelling a subset of obese people as metabolically healthy, physicians may not feel pressed to curb the current obesity epidemic and prevent the next generation of people from becoming obese. Another issue is that the most commonly used anthropometric index to define obesity, the body mass index, is at the core of the controversy because of its limitations including its inability to discriminate between fat mass and muscle mass. Many recent epidemiological and metabolic studies have used other indices such as waist-hip ratio, waist circumference, and imaging (computed tomography or magnetic resonance imaging) measurements of visceral adiposity and of ectopic fat depots. In addition, emerging evidence supports the importance of cardiorespiratory fitness, skeletal muscle mass and strength in patients with obesity as useful variables to predict CVD risk beyond adiposity. In this review, we will discuss the complex and disparate effects of obesity on CVD, particularly focusing on whether, under given circumstances, it could be harmful, potentially harmless or neutral, or even possibly protective.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it