To What Extent Do Clinical Practice Guidelines for Chronic Diseases Embrace Current Obesity Management Guidance? A Qualitative Content Analysis
Bibliographic record
Abstract
Introduction: Obesity is a chronic, progressive, and recurring disease that contributes significantly to multi-morbidity across Europe. Despite the publication of numerous clinical practice guidelines (CPGs) for obesity, many chronic disease guidelines for obesity-related diseases such as diabetes, MASLD, heart disease, and obstructive sleep apnoea do not integrate contemporary understandings of obesity as an adiposity-based disease requiring direct management in its own right. The objective of this qualitative content analysis was to evaluate the extent to which recent chronic disease CPGs align with current evidence-based obesity guidance. METHODS: A working group convened by the European Association for the Study of Obesity reviewed 13 chronic disease CPGs published since 2019. Guidelines were assessed using nine predefined criteria based on leading obesity CPGs. Data were extracted, and content analysis was used to identify gaps and opportunities across the chronic disease CPGs. RESULTS: Three key themes were identified: (1) inconsistent scientific/medical conceptualization of obesity, (2) limited integration of evidence-based obesity management guidance, and (3) minimal inclusion of person-centred care principles. Most guidelines treated obesity as a risk factor, not a disease, and lacked reference to contemporary obesity frameworks or person-first language. CONCLUSION: Greater alignment across CPGs is essential to improve obesity care within multi-morbidity management. Collaborative, cross-speciality approaches are recommended to harmonize clinical guidance and promote integrated, stigma-free care. .
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 | 0.126 | 0.244 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".