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Record W3197250216 · doi:10.5772/intechopen.99815

Metformin: Pros and Cons

2021· book-chapter· en· W3197250216 on OpenAlexaboutno aff
Shalini Sivadasan, Muthukumar Subramanian

Bibliographic record

VenueIntechOpen eBooks · 2021
Typebook-chapter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolism, Diabetes, and Cancer
Canadian institutionsnot available
Fundersnot available
KeywordsMetforminMedicineInternal medicineLactic acidosisEndocrinologyType 2 Diabetes MellitusDiabetes mellitusType 2 diabetesPolycystic ovaryInsulinInsulin resistance

Abstract

fetched live from OpenAlex

Metformin was approved for the treatment of Type 2 Diabetes Mellitus in 1958 for UK, in 1972 for Canada and in 1995 by FDA in USA. Metformin is the drug of choice for patients who are obese and have type 2 diabetes mellitus. Though metformin was at first proven to treat hyperglycemia, many other uses of metformin are proven to be effective. It is also used for gestational diabetes mellitus, obesity, hyper secretion of ovarian androgen, poly-cystic ovary syndrome (PCOS), anti-psychotic therapy induced weight gain, cancer treatment and anti-aging. Metformin causes a decrease in appetite thus known to act on obesity. The other action of metformin is reduction of circulating levels of insulin and insulin like growth factor 1 (IGF-1) which is associated with anticancer action. There are ongoing researches about the effect of metformin on anti-aging properties and proved that metformin is linked with anti-aging factors. Three main factors that are related with aging are oxidation, glaciation and methylation. Metformin as all drugs, have unwanted effects as well. Many side effects of metformin are considered mild where lactic acidosis and vitamin B12 deficiency happens to be the major.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.037
Threshold uncertainty score0.125

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0370.023

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 head0.237
Teacher spread0.223 · 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 designNot applicable
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

Citations5
Published2021
Admission routes1
Has abstractyes

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