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Record W1791680780 · doi:10.3968/6621

Ramadan and Type 2 Diabetes in Bangladesh

2015· article· en· W1791680780 on OpenAlexvenueno aff
Sharmin Hossain, Kazi Rumana Ahmed, Farzana Saleh, Liaquat Ali

Bibliographic record

VenueAdvances in natural science/Advances in natural sciences · 2015
Typearticle
Languageen
FieldMedicine
TopicDietary Effects on Health
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCreatinineDiabetes mellitusInternal medicineBody mass indexType 2 diabetesWaistTriglycerideFructosamineProspective cohort studyEndocrinologyCholesterol

Abstract

fetched live from OpenAlex

Ramadan is the Holy month of the Muslims when they are required to fast from dawn to sunset. The present study was aimed at exploring the effects of fasting in the Ramadan on body mass index (BMI), waist circumference (WC), total cholesterol, triglyceride (TG), creatinine, and acute complications in patients with type 2 diabetes in Bangladesh. This prospective study was carried out among 92 types 2 diabetes patients (age 47±9 years, mean±SD), selected randomly from a tertiary-care hospital of the Diabetic Association of Bangladesh (BADAS). Significant changes were observed in the pulse rate (p=.001) and BMI (p=.001) in type 2 diabetes during Ramadan. Fasting blood glucose (p=.001), fructosamine (p=.001), TC (p=.003), HDL-C (p=.004), TG (p=.04), and creatinine (p=.01) were significantly higher in all the patients during fasting. Fasting during Ramadan is associated with a deterioration of metabolic control in diabetic patients. However, fasting did not cause any irreversible damage to renal function; so, fasting is safe for type 2 diabetes in Bangladesh during Ramadan.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.346
Teacher spread0.333 · 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 source (direct Gemma or distilled Codex), 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
Published2015
Admission routes1
Has abstractyes

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