Assessing the state of obesity care: Quality, access, guidelines, and standards
Bibliographic record
Abstract
Background: An international panel of obesity medicine experts from multiple professional organizations examined patterns of obesity care and current obesity treatment guidelines to identify areas requiring updating in response to emerging science and clinical evidence. Aims: The panel focused on multiple medical health and societal issues influencing effective treatment of obesity and identified several unmet needs in the definition, assessment, and care of obesity. Methods: The panel was held in Leesburg, Virginia in September 2019. Results: The panelists recommended addressing these unmet needs in obesity medicine through research, education, evaluation of delivery and payment of care, and updating clinical practice guidelines (CPG) to better reflect obesity's pathophysiological basis and heterogeneity, as well as the disease's health, sociocultural, and economic complications; effects on quality of life; need for standards for quantitative comparison of treatment benefits, risks, and costs; and the need to more effectively integrate obesity treatment guidelines into routine clinical practice and to facilitate more direct clinician participation to improve public understanding of obesity as a disease with a pathophysiological basis. The panel also recommended that professional organizations working to improve the care of people with obesity collaborate via a working group to develop an updated, patient-focused, comprehensive CPG establishing standards of care, addressing identified needs, and providing for routine, periodic review and updating. Conclusions: Unmet needs in the definition, assessment and treatment of obesity were identified and a blueprint to address these needs developed via a clinical practice guideline that can be utilized worldwide to respond to the increasing prevalence of obesity.
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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 | 0.255 | 0.444 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.006 | 0.015 |
| 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".