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Record W1996603934 · doi:10.1186/s12916-014-0136-z

Type 2 diabetes and health care costs in Latin America: exploring the need for greater preventive medicine

2014· article· en· W1996603934 on OpenAlexaff
Armando Arredondo

Bibliographic record

VenueBMC Medicine · 2014
Typearticle
Languageen
FieldMedicine
TopicDiabetes, Cardiovascular Risks, and Lipoproteins
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsMedicineLatin AmericansHealth careEpidemiological transitionDiseasePopulationPreventive healthcareNatural historyEpidemiologyGerontologyPublic healthEnvironmental healthEconomic growthNursingPathology

Abstract

fetched live from OpenAlex

BACKGROUND: Despite advances in medicine, health systems in Latin America are not coping with the challenges of chronic diseases. Incidence of disease and the economic burdens as a consequence have both increased in recent years. We have chosen Type 2 diabetes as an example to highlight the challenges posed by chronic diseases, in terms of the epidemiological transition and the economic burden of the demand for services to treat such problems. DISCUSSION: Current health systems are not prepared to respond in a comprehensive manner to all phases of the natural history of the disease. There are new models of universal coverage, but resources and models of care are focused on programs aimed at healing/rehabilitation, and very sparsely at detection/prevention. SUMMARY: In this scenario, chronic problems have alarmingly increased direct costs (medical care) and indirect costs (temporary disability, permanent disability and premature mortality). If more resources are not assigned to preventive medicine, these trends, in addition to not meeting the needs of the population, will financially collapse health systems and the patients' pockets. This Opinion piece outlines some possible changes that can be implemented to better prepare the health services in Latin American countries.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.875
Threshold uncertainty score0.574

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.060
GPT teacher head0.301
Teacher spread0.241 · 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

Citations38
Published2014
Admission routes1
Has abstractyes

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