Diabetes mellitus tipo 2 en Perú: una revisión sistemática sobre la prevalencia e incidencia en población general
Bibliographic record
Abstract
Objetives. To identify prevalence and incidence studies of type 2 diabetes mellitus in the general adult population in Peru. MATERIALS AND METHODS: Observational studies involving randomly selected individuals from the general population were evaluated. The definition of diabetes had to include at least one laboratory parameter (e.g. baseline glucose). LILACS, SciELO, Scopus, Medline, Embasem and Global Health were reviewed without restriction. Risk of bias was assessed using the Newcastle-Ottawa scale. RESULTS: The search identified 909 results; additionally, an article from another source was added. After evaluating the results, 20 articles representing nine studies were selected (n=16 585). One of the studies was national in scope and another semi-national (ENINBSC, 2004-05 and PERUDIAB, 2010-12). The first study reported a prevalence of 5.1% in subjects ≥35 years, while the second reported 7.0% in subjects ≥25 years. Other studies focused on populations in one or more cities in the country, or on selected population groups, such as the PERU MIGRANT study (2007-08) which reported the prevalence of diabetes in subjects in rural areas (0.8%), in rural-urban migrants (2.8%), and in urban areas (6.3%). Three studies followed up prospectively, one of them being PERUDIAB: a cumulative incidence of 19.5 new cases per 1,000 people per year. The risk of bias was low in all studies. CONCLUSIONS: Population studies indicate that the prevalence of diabetes has increased and that there are approximately two new cases per 100 people per year. Evidence is still scarce in the jungle and in rural populations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.022 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".