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Record W2804581143 · doi:10.1177/2156587217753004

Diabetes Control: Is Vinegar a Promising Candidate to Help Achieve Targets?

2018· article· en· W2804581143 on OpenAlexaff
Fahad Javaid Siddiqui, Pryseley Nkouibert Assam, Nurun Nisa de Souza, Rehena Sultana, Rinkoo Dalan, Edwin Chan

Bibliographic record

VenueJournal of Evidence-Based Integrative Medicine · 2018
Typearticle
Languageen
FieldMedicine
TopicDietary Effects on Health
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsPostprandialMedicineConfidence intervalDiabetes mellitusRandomized controlled trialClinical trialAdverse effectInternal medicineInsulinEndocrinology

Abstract

fetched live from OpenAlex

BACKGROUND: Renewed interest in vinegar as a glucose-lowering agent led to several small trials in the recent past. However, none of the trials could independently provide sufficient evidence. OBJECTIVES: Our review aimed to obtain reliable estimates of effects of vinegar on short-term and long-term blood glucose control. METHODS: Large bibliographic databases were searched from inception to date of search without language and publication date restrictions. All clinical trials evaluating effect of vinegar on diabetes mellitus patients were eligible. Two authors independently extracted data on fasting and 2-hour postprandial blood glucose, insulin, and HbA1c levels at the various time points. MS Excel, SAS® v9.3, and RevMan v5.3 were used for data analysis. RESULTS: = 0%). Other long-term outcomes favored vinegar but were not significant. Short-term outcomes showed significantly lower pooled mean difference in glucose levels at 30 minutes in the vinegar group. Readings at 60, 90, and 120 minutes were lower in the vinegar group but not statistically significant. Adverse effects profile also favored the vinegar group. CONCLUSIONS: It is worthwhile to carry out carefully planned large trails to determine the efficacy and effectiveness of vinegar as an adjunct treatment modality.

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)0.004
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.325
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.069
GPT teacher head0.374
Teacher spread0.305 · 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 designBench or experimental
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

Citations19
Published2018
Admission routes1
Has abstractyes

Explore more

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