SIO management algorithm for patients with overweight or obesity: consensus statement of the Italian Society for Obesity (SIO)
Bibliographic record
Abstract
In approaching the treatment of obesity, three major caveats, specific to this complex disease, need to be taken into consideration in order to avoid over-simplification. \nFirstly, obesity definition is currently based on the body mass index (BMI). However, BMI has two major limitations: it is not a measure of fat mass, and it does not convey any information on fat distribution and regional fat depots. These limitations are well known by the scientific community that is struggling to find ways to move beyond BMI in obesity classification. \nSecondly, for the reasons specified above, the development of comorbidities or complications, which occur in the vast majority of obese patients during the course of the disease, is not always linearly correlated with BMI. Many variables contribute to their manifestation beyond the degree of obesity: duration of disease, age, sex, fat distribution, genetic background, the degree of mechanical disability, etc. \nThirdly, treatment options are now quite few. Their indications should take into account the severity of obesity together with the presence and severity of complications and age, in order to grade interventions; these varying from therapeutic lifestyle changes to bariatric surgery. \nIn order to provide a staging system able to help clinicians in phenotyping obese patients, beyond BMI, Sharma and Kushner [1] developed the so-called EOSS (Edmonton Obesity Staging System) composed of the following five stages: \n \n 0.No apparent obesity-related risk factors (e.g., blood pressure, serum lipids, fasting glucose, etc., within normal range), no physical symptoms, no psychopathology, no functional limitations and/or impairment of well-being. \n 1. Presence of obesity-related subclinical risk factors (e.g., borderline hypertension, impaired fasting glucose, elevated liver enzymes, etc.), mild physical symptoms (e.g., dyspnea on moderate exertion, occasional aches and pains, fatigue, etc.), mild psychopathology, mild functional limitations, and/or mild impairment of well-being. \n 2.Presence of established obesity-related chronic disease (e.g., hypertension, type 2 diabetes, sleep apnea, osteoarthritis, reflux disease, polycystic ovary syndrome, anxiety disorder, etc.), moderate limitations in activities of daily living and/or well-being. \n 3. Established end-organ damage such as myocardial infarction, heart failure, diabetic complications, incapacitating osteoarthritis, significant psychopathology, significant functional limitations, and/or impairment of well-being. \n 4.Severe (potentially end-stage) disabilities from obesity-related chronic diseases, severe disabling psychopathology, severe functional limitations, and/or severe impairment of well-being. \nThe EOSS has been validated as a system able to identify patients at increased mortality risk who therefore deserve more clinical and therapeutic attention [2]. \nWe have taken advantage of this now well-established staging system to develop a therapeutic algorithmic chart (Fig. 1) that includes BMI, age and EOSS stages. At each intersection, a color code identifies the proposed preferred treatment option. Obviously, treatment options are not mutually exclusive, but have to be understood as additive (e.g., a patient eligible for bariatric surgery should continue to follow therapeutic lifestyle changes and, if needed, pharmacotherapy). \nTreatment algorithm chart that takes advantage of the EOSS (Edmonton Obesity Staging System, see text and Ref. [1]). At each intersection a color code identifies the proposed preferred treatment option. Obviously, treatment options are not mutually exclusive ... \nStrengths and limitations \nThe strength of the EOSS system relies on its ability to better identify patients who are at increased risk of mortality [2]. The limitations of the EOSS system have been clearly highlighted by Sharma and Kushner in their review paper [1]. They recognize that definitions of some risk factors are subject to change. Furthermore, the EOSS system includes subjective parameters, such as psychological impact or functional performance, the assessment of which may vary among clinicians. In this regard, attention should be drawn to the vagueness of certain definitions such as mild psychopathology, anxiety disorder, significant psychopathology, and severe disabling psychopathology. In addition, the lack of any reference to eating disorders, in particular binge eating disorder, should be pointed out which since 2013 has been considered an autonomous diagnostic category by DSM-5. \nBy integrating the EOSS system, our therapeutic algorithmic chart includes its pros and cons. In addition, a specific limitation of our chart is the lack of evidence-based data.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".