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Record W2194015324 · doi:10.13034/jsst.v8i2.78

Diabetes Mellitus Complications in Sub-Saharan Africa

2015· article· en· W2194015324 on OpenAlexvenueno aff
Petra Famiyeh

Bibliographic record

VenueJournal of Student Science and Technology · 2015
Typearticle
Languageen
FieldMedicine
TopicDiabetes, Cardiovascular Risks, and Lipoproteins
Canadian institutionsnot available
Fundersnot available
KeywordsDiabetes mellitusMedicineDiseaseIntensive care medicineInternal medicineEndocrinology

Abstract

fetched live from OpenAlex

Diabetes mellitus, commonly known as diabetes, is a non-communicable disease whereby a person’s pancreas is either incapable of producing or unable to use insulin in the body. The disease and its complications are growing in different parts of the world. It has been predicted that by 2035, there will be over 205 million diabetics in the world. It has been hypothesized that diabetes complications is highly prevalent in many African countries due to high medication cost, lack of early diagnoses and treatment, low economic standing, and culture-influenced beliefs about the disease. To address this hypothesis, a literary review was conducted on peer-reviewed articles. PubMed and Google Scholar were searched using the keywords: diabetes, diabetes in Africa, diabetes complications to retrieve these articles. The articles were read and evaluated by one reviewer and information was extracted to generate conclusion about the hypothesis. The research found that the high influx of diabetes complications in Sub-Saharan Africa is correlated with the low economic status of countries within this region. Also high reliance on traditional medicine leading to delayed treatment also influences the prevalence of complications.This research also sought to identify the most effective preventative measures for these complications (e.g. optimal diet, exercise, access to effective medications) and the availability of these measures in the Sub-Saharan African regions. It was determined that countries in Sub-Saharan Africa lacked access to optimal medications, which is the most effective preventative measure. Future studies should focus on ways to improve this preventive measure to optimize diabetes control in these regions.Le diabète sucré, connu communément sous le nom de diabète, est une maladie non-transmissible qui surgit lorsque le pancréas d’une personne est incapable de produire de l’insuline ou est incapable d’utiliser l’insuline produite. Des prévisions montrent qu’à l’an 2035 , il y aura plus de 205 millions de diabétiques dans le monde. Il y a une hypothèse que la situation économique de pays subsahariens et la culture contribuent aux complications diabétiques de ces régions. De la recherche a été conduite dans des journaux révisé par des pairs afin de prouver ou de réfuter cette hypothèse. Il a été découvert qu’il y a une corrélation directe entre le statut économique et culturel de pays en Afrique subsaharienne et le taux élevé de complications provoqués par le diabète sucré. Cette recherche a aussi été ciblée pour rechercher les mesures préventatives pour éviter ces complications et pour augmenter la disponibilité de ces mesures. Il a été déterminé que ces pays manquaient ces mesures préventives et efficaces. Des études futures devraient se concentrer sur des moyens d’installation de ces mesures afin de sauver des vies.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.012
Science and technology studies0.0010.000
Scholarly communication0.0030.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.028
GPT teacher head0.292
Teacher spread0.264 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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