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Record W2146041442 · doi:10.31729/jnma.240

Three Patterns of Rising type 2 Diabetes Prevalence in the World: Need to Widen the Concept of Prevention in Individuals into Control in the Community

2009· article· en· W2146041442 on OpenAlexaboutno aff
Madhur Dev Bhattarai

Bibliographic record

VenueJournal of Nepal Medical Association · 2009
Typearticle
Languageen
FieldMedicine
TopicBirth, Development, and Health
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineObesityPopulationBody mass indexIndigenousGerontologyDemographyType 2 diabetesEnvironmental healthDeveloping countryOverweightDiabetes mellitusEconomic growthEndocrinology

Abstract

fetched live from OpenAlex

This paper analyses the patterns of rising type 2 diabetes prevalence in the world with their plausible reasons focusing on control measures. It shows existence of combinations of three patterns of rises, viz. gradual, rapid and accelerated, leading to prevalence of 4-9% now in Europids, 14-20% in migrant or urbanized Asian Indians, Arabs, Chinese, Africans, and Hispanics and above 30-50% in indigenous peoples of Canada, USA, Australia and Pacific regions. It demonstrates that though ageing, sedentary life and obesity of people explain gradual rise in Europids, effects of rapid transition in nutritional status of population and of maternal hyperglycaemia on the risk of offspring developing glucose intolerance further add to rapid and accelerated rises respectively. It recommends that current approach of primary prevention of diabetes in people, particularly with impaired glucose tolerance, advocating modest loss of excess weight and moderate-intensity exercise, be widen into concept of control in community covering rapid and accelerated rises. The control programmes essentially are vigorous educational campaign and planning to improve nutritional status of women of childbearing age in rural and poorer sectors of society and to keep weight of adults within recommended body mass index (BMI) range, like 18.5-22.9 kg/m2 for Asian and other similar populations. The population-based approaches with examples, considering developing countries, are outlined. The paper emphasizes the importance of keeping prepregnancy weight optimum, preferably below the middle of recommended BMI range, to avoid even sub-clinical maternal hyperglycemia, for prevention and control of accelerated rise in any population.

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)0.011
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.463

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.024
GPT teacher head0.338
Teacher spread0.314 · 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.

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

Citations36
Published2009
Admission routes1
Has abstractyes

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