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Record W2222239597 · doi:10.20286/nova-jmbs-030312

Ilam Lipid and Glucose Study: A cross-sectional Epidemiologic Study

2014· article· en· W2222239597 on OpenAlexvenueno aff
Mohamad Reza Havasian, Jafar Panahi, Mohammad Ali Ruzegar

Bibliographic record

VenueNova Journal of Medical and Biological Sciences · 2014
Typearticle
Languageen
FieldMedicine
TopicDiabetes, Cardiovascular Risks, and Lipoproteins
Canadian institutionsnot available
Fundersnot available
KeywordsCross-sectional studyEnvironmental healthMedicineInternal medicinePathology

Abstract

fetched live from OpenAlex

Development of urbanization and alteration in the lifestyle and age structure of the population (aging) increase the risk of non-communicable diseases (cardiovascular disease, diabetes, and stroke). The aim of this study was to evaluate blood glucose and lipid profile, and also estimate the percentage of people with lipids and fasting blood glucose disorders in the city of Ilam. This research is a retrospective study. Therefore, in this study data collecting was performed from clinical laboratories in Ilam from 2006 to 2012. The sample size was determined by the Woodward's formula and all the statistical analyzes were performed using the SPSS-18 software. The results showed that the average amount of cholesterol, triglycerides, FBG, HDL-C, and LDL-C was equal to 181 ± 21, 151 ± 17, 86 ± 7, 61 ± 8, and 21 ± 3 mg/dl, respectively. Also 25.2% of participants have Cholesterol disorder, 20.3% triglyceride disorder, 12.1% impaired fasting glucose, 18% HDL-C disorder, and 15.8% LDL-C disorder. This study shows that the mean level of FBG and lipid profile in Ilam is almost identical to the normal values in western societies. Also, the prevalence of impaired FBG and plasma lipids disorders were relatively high in this city. Given these results, a health plan should be developed and the necessary training must be given to the people to reduce of these disorders.

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)0.016
metaresearch head score (Gemma)0.006
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 score0.221
Threshold uncertainty score0.745

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.121
GPT teacher head0.390
Teacher spread0.269 · 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

Citations2
Published2014
Admission routes1
Has abstractyes

Explore more

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