Obesity in South and Southeast Asia—A new consensus on care and management
Bibliographic record
Abstract
Obesity is a chronic disease in which the abnormal or excessive accumulation of body fat leads to impaired health and increased risk of mortality and chronic health complications. Prevalence of obesity is rising rapidly in South and Southeast Asia, with potentially serious consequences for local economies, healthcare systems, and quality of life. Our group of obesity specialists from Bangladesh, Brunei Darussalam, India, Indonesia, Malaysia, Philippines, Singapore, Sri Lanka, Thailand, and Viet Nam undertook to develop consensus recommendations for management and care of adults and children with obesity in South and Southeast Asia. To this end, we identified and researched 12 clinical questions related to obesity. These questions address the optimal approaches for identifying and staging obesity, treatment (lifestyle, behavioral, pharmacologic, and surgical options) and maintenance of reduced weight, as well as issues related to weight stigma and patient engagement in the clinical setting. We achieved consensus on 42 clinical recommendations that address these questions. An algorithm describing obesity care is presented, keyed to the various consensus recommendations.
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 | 0.015 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| 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".