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Record W2338982513 · doi:10.4103/2230-8229.181006

Ramadan fasting is not usually associated with the risk of cardiovascular events: A systematic review and meta-analysis

2016· review· en· W2338982513 on OpenAlexaff
Tanvir Chowdhury Turin, Salim Said Ahmed, Nusrat Shommu, Arfan R. Afzal, Mohammad Al Mamun, Mahdi Qasqas, Nahid Rumana, Marcus Vaska, Noureddine Berka

Bibliographic record

VenueJournal of Family and Community Medicine · 2016
Typereview
Languageen
FieldMedicine
TopicDietary Effects on Health
Canadian institutionsCalgary Laboratory ServicesUniversity of CalgaryAlberta Health ServicesFoothills Medical Centre
Fundersnot available
KeywordsMedicineMeta-analysisMyocardial infarctionIncidence (geometry)Stroke (engine)Heart failureDiseaseInternal medicineIntensive care medicine

Abstract

fetched live from OpenAlex

Over one billion Muslims worldwide fast during the month of Ramadan. Ramadan fasting brings about some changes in the daily lives of practicing Muslims, especially in their diet and sleep patterns, which are associated with the risk of cardiovascular diseases. Over the years, many original studies have made the effort to identify the possible impact of the Ramadan fast on cardiovascular diseases. This systematic review and meta-analysis is an attempt to present the summary of key findings from those articles and an appraisal of selected literature. A systematic search using keywords of ";Ramadan fasting" and ";cardiovascular diseases" was conducted in primary research article and gray-literature repositories, in combination with hand searching and snow balling. Fifteen studies were finally selected for data extraction on the outcomes of stroke, myocardial infarction, and congestive heart failure. The analysis revealed that the incidence of cardiovascular events during the Ramadan fast was similar to the nonfasting period. Ramadan fast is not associated with any change in incidence of acute cardiovascular disease.

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)0.026
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (broad), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.223
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0260.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0180.003
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.161
GPT teacher head0.378
Teacher spread0.216 · 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 designMeta-analysis
Domainnot available
GenreReview

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

Citations31
Published2016
Admission routes1
Has abstractyes

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