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Record W4309883801 · doi:10.54097/hset.v19i.2662

Effects of Taking Magnesium Supplements for Diabetics and Those with A High Risk of Diabetes

2022· article· en· W4309883801 on OpenAlexaff
Jinling He

Bibliographic record

VenueHighlights in Science Engineering and Technology · 2022
Typearticle
Languageen
FieldNursing
TopicMagnesium in Health and Disease
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsInsulin resistanceDiabetes mellitusMedicineBlood sugarInternal medicineInsulinEndocrinologyMagnesiumRisk factorSugarBiologyBiochemistryChemistry

Abstract

fetched live from OpenAlex

Diabetes is a common disease occurring around the world. Patients usually have high blood sugar and many complications. Diabetes is typically caused by an inability to use insulin or damage to beta-cells. Magnesium is a co-factor involved in glycolysis and activation of insulin use, so by examining trials including magnesium supplementation in individuals with all forms of diabetes, this research investigated if taking magnesium supplements had an association with blood glucose and insulin resistance in diabetics or those with a high risk of diabetes. Ten trials in this paper are analyed to figure out whether magnesium supplement is associated with blood sugar and insulin resistance in diabetics and those with high risk of this diseased. Eight trials showed an association between consuming Mg supplement and lower blood sugar and insulin resistance, but two trials concluded there was no association. The differences in the results may be caused by different blood magnesium levels or ethnic groups of participants, which needs further analysis.

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)0.002
metaresearch head score (Gemma)0.010
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.004
GPT teacher head0.223
Teacher spread0.219 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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