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Record W4396679148 · doi:10.4103/ijnpnd.ijnpnd_12_24

Age, body composition analysis, and gender differences of morbidly obese Omani subjects

2024· article· en· W4396679148 on OpenAlexaff
Juhaina Al-Maskari, Bader Al-Hadhrami, Mostafa I. Waly, Lyutha Al-Subhi, Amanat Ali

Bibliographic record

VenueInternational journal of Nutrition Pharmacology Neurological Diseases · 2024
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsMorbidly obeseMedicineBody mass indexObesityIncidence (geometry)DemographyPopulationInternal medicineWeight lossEnvironmental health

Abstract

fetched live from OpenAlex

Background: The World Health Organization has indicated that Gulf countries, including Oman, have the highest incidence of obesity. Objectives: This study aims to describe the changes in body composition values as an index among the morbidly obese population. We investigated the gender, age, and body mass index (BMI)-related differences in morbidly obese subjects. Methods: A retrospective, hospital-based study was carried out at the Royal Hospital, Muscat. Results: The study involved 104 morbidly obese subjects (35 males and 69 females) with a BMI ≥35 kg/m 2 . All enrolled study subjects were compared for their gender, BMI, and age. Significant trends were observed for body fat percentage, water percentage, muscle mass, basal metabolic rate, bone mass and visceral fat between different genders in general, and between different genders within the same age frame ( P < 0.05). No significant differences were observed within the same gender regarding changes in BMI and weight gain ( P > 0.05). Conclusion: Aging and changes in BMI have a significant effect on the body composition of morbidly obese individuals of different genders while showing no significant effects within the same genders.

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 score0.344
Threshold uncertainty score0.587

Codex and Gemma teacher scores by category

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

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 head0.052
GPT teacher head0.398
Teacher spread0.347 · 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

Citations3
Published2024
Admission routes1
Has abstractyes

Explore more

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