MétaCan
Menu
Back to cohort
Record W6989823759

Causes, Symptoms, Diagnosis and Treatment of Obesity in Young People Today

2024· article· en· W6989823759 on OpenAlexaboutno aff

Bibliographic record

VenueUmsida Repository (Universitas Muhammadiyah Sidoarjo) · 2024
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Disease and Adiposity
Canadian institutionsnot available
Fundersnot available
KeywordsObesityOverweightUnderweightScopusQuarter (Canadian coin)Global healthDouble burden
DOInot available

Abstract

fetched live from OpenAlex

This paper provides information about the causes, symptoms, diagnosis, and treatment methods of adolescent obesity in the modern era. In the modern world, more people die from the consequences of overweight and obesity than from the consequences of underweight (official website of the World Health Organization) [1]. The global obesity epidemic continues to gain momentum, currently affecting more than two billion people -about a quarter of the world's population. In 2017, the Global Burden of Disease Group stated: "Since 1980, the number of people with obesity has doubled in more than 70 countries and is steadily increasing in all remaining countries" [2]. UNICEF reported in 2017 that over the past 15 years, there has been no progress in reducing the number of children and adolescents with overweight and obesity [3]. According to leading experts in this field, if the trend of the 2000s continues, the likelihood of achieving the goal of halving the total number of obese people by 2025 is close to zero [4]. It is safe to say that humanity is currently losing the war on obesity. Russian and international databases were used to search for articles: RSCI, PubMed, ScienceDirect, Scopus and Google Scholar. The search was conducted using the keywords: obesity, globesity, normal-weight obesity, BMI, obesity, obesity epidemic, hidden obesity, BMI. The analysis included original studies and review articles published since 2018 to 2023 in English or Russian. Only full-text versions of articles were used. In 1988, the WHO reported on the problem of obesity as a global phenomenon, and for the first time, the conclusion about the obesity epidemic in the world was voiced [3]. Since then, the term "obesity epidemic or pandemic" has firmly entered into scientific circulation, reports of official bodies and the media. However, it is unclear whether the word "epidemic" defines what we observe in the modern human population. An epidemic begins with an outbreak of a disease, passes a peak, then there is a decline, and eventually it ends, as all susceptible individuals either recover or die. Instead of a sharp outbreak of the "disease" of obesity, we observe a constant and steady increase in the proportion of people with a high body mass index (BMI). Historical evidence from epidemiological studies allows us to conclude that the increase in BMI in the human population has been occurring over the past 300 years. American economist Robert Fogel has studied the relationship between body size and labor productivity since the early 18th century [12]. Using data on the length and weight of residents of the most economically developed countries (Scandinavian countries, France, Great Britain) from 1705 to 1975, Fogel showed that in 1705 the average BMI in these populations was 19 kg/m2, which is below the WHO recommended ideal BMI value of 22 kg/m2. Over the next three centuries, BMI gradually increased, reflecting the increase in average population values of length and weight, and by 1975 it was 25 kg/m2. After reaching the upper limit of normal values, BMI in many developed countries continued to increase and by 2014 in the United States it was 27.8 kg/m2. It is suggested that the observed increase in BMI is not an epidemic, but a natural result of biological processes to increase body size in order to protect against hunger and improve efficiency in various conflicts, including military ones. Simultaneously with the continuing weight gain, a slowdown or complete stop in the increase in body length in modern humans is recorded

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score1.000

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 studies0.0000.000
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 head0.007
GPT teacher head0.213
Teacher spread0.206 · 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.

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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueUmsida Repository (Universitas Muhammadiyah Sidoarjo)Same topicCardiovascular Disease and AdiposityFrench-language works237,207