The urgency in proposing the optimal obesity cutoff value in Indonesian population: A narrative review
Bibliographic record
Abstract
In developing nations such as Indonesia, obesity and central obesity have emerged as major public health issues. Many studies have revealed that morbidity and death from obesity-related diseases are already significant in some "Asian" communities at low body mass index (BMI) levels. A recent study showed that the obesity prevalence in Indonesia is underestimated when using the current BMI cutoff (obese ≥ 27.0). Indonesia faced an increase in obesity-related chronic diseases despite having a lower obesity prevalence than developed countries, which may be explained by the underestimation of obesity levels in Indonesia. This creates a huge global health problem, as well as an economic burden. Another recent study on the Indonesian population depicted the new proposed cutoff of waist circumference (WC), which is lower than the World Health Organization (WHO) standard for detecting the early detection of type 2 diabetes mellitus (T2DM), one of the comorbidities and a strong correlation with obesity. An analysis of 58 studies in 2021 that included Indonesian adult subjects revealed enormous differences and ambiguities in defining obesity cutoffs values among Indonesian researchers. Additionally, we advocate adding the Edmonton Obesity Staging System (EOSS) staging to the anthropometric classification for a better clinical evaluation of obesity. Considering the urgency of obesity determination in Indonesia for clinical application and study purposes, this review highlights the need to revise the optimal cutoff value for obesity to warrant early prevention and control of diabetes complications.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".