Applicability of obesity clinical practice guidelines in low- and middle-income countries
Bibliographic record
Abstract
Obesity is a chronic disease and its impact on individuals and society is a major global health problem, with a high prevalence across all socio-economic strata. Some specialty societies include obesity management related recommendations in clinical practice guidelines, but relatively few guidelines are specifically designed to fully address its diagnosis and management. We sought to understand clinicians' use of obesity clinical practice guidelines in their practice, the perceived deficiencies and implementation barriers, and differences between those practicing in high-income countries (HIC) and those in low-/middle-income countries (LMIC). An email survey of physicians in the Translational Medicine Academy database was offered from August 26 to December 26, 2024 to inquire about participants' demographic information, experience, and views of obesity guidelines as related to their practice. Of 1,412 participating clinicians from 129 countries, 741 partially completed, and an additional 671 fully completed the survey: 281 practiced in HIC; 1,130 in LMIC. Obesity was recognized as a disease (93.5% of respondents) as was its impact on other disorders: cardiovascular disease ranked as the most important, and hepatic disorders the least, with no differences between HIC and LMIC. Only 13.1% regarded the guidelines as equally applicable across different economic strata and geography, and just 29% thought the guidelines to be applicable in their country, with no difference between HIC and LMIC. The most frequently indicated reason given for hindering implementation of obesity guidelines was that they were primarily relevant for HIC; the most common local factor hindering implementation was cost, with no difference in views between HIC and LMIC. There was broad agreement (83.4%) for the importance/need for specific recommendations for patients of differing socio-economic status, with no difference between HIC (79.4%) and LMIC (84.3%; p=0.191), and for guideline authors to include those from LMIC (68.7%), with those from LMIC agreeing more strongly (73.1%) than did those from HIC (50.4%; p<0.00001). Most clinicians from both HIC and LMIC do not consider obesity guidelines to be applicable in their country, and appear to have minimal recognition of obesity’s impact on hepatic disorders, suggesting a need for improved clinician education and awareness. It was widely recognized that guidelines should have specific recommendations directed at differing socio-economic environments, and writing committees including authors from those settings.
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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 | 0.022 | 0.092 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".