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Record W2336430090 · doi:10.1007/s40519-016-0279-3

SIO management algorithm for patients with overweight or obesity: consensus statement of the Italian Society for Obesity (SIO)

2016· article· en· W2336430090 on OpenAlexaboutno aff
Ferruccio Santini, Luca Busetto, Barbara Cresci, Paolo Sbraccia

Bibliographic record

VenueEating and Weight Disorders - Studies on Anorexia Bulimia and Obesity · 2016
Typearticle
Languageen
FieldMedicine
TopicBariatric Surgery and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsObesityOverweightBody mass indexMedicinePsychological interventionDiseaseClassification of obesityGerontologyInternal medicineFat massPsychiatry

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.627

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.255
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations21
Published2016
Admission routes1
Has abstractyes

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