iCARDIO Alliance Global Implementation Guidelines for the Management of Obesity 2025 - Focus on prevention and treatment of cardiometabolic disease
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
There are a number of guidelines on how to manage obesity, but inconsistencies in healthcare access, varying infrastructure, resource constraints and diverse local practices restrict their global applicability. This underscores the need for universal recommendations that address the unique challenges faced by patients and healthcare providers worldwide. Our Global Guidelines emphasize the incorporation of novel therapies, while integrating standards of care with the most up-to-date evidence to enable clinicians to optimize obesity management. Context-specific recommendations tailored to individual patient needs are highlighted, providing a thorough evaluation of the risks, benefits, and overall value of each therapy, aiming to establish a standard of care that improves patient outcomes and reduces the burden of hospitalization in this susceptible population. These Global Guidelines provide evidence-based recommendations that represent a group consensus considering the many other published guidelines that have reviewed many of the issues discussed here, but they also make new recommendations where new evidence has recently emerged, and – most importantly – also provide recommendations on several issues where resource limitations may put constraints on the care provided to patients living with obesity. Such “economic adjustment” recommendations aim to guide situations when “Resources are somewhat limited” or when “Resources are severely limited”. Hence, this document presents a comprehensive update to obesity management guidelines, thereby aiming to provide a unified strategy for the pharmacological, non-pharmacological, and invasive management of this significant global health challenge that is applicable to the needs of healthcare around the globe.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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