Prevalencia e incidencia de hipertensión arterial en Perú: revisión sistemática y metaanálisis
Bibliographic record
Abstract
OBJECTIVE: . To determine the prevalence and incidence of arterial hypertension, as well as the prevalence of previous diagnosis of arterial hypertension (self-reported) among the adult population of Peru. MATERIALS AND METHODS.: Systematic review and meta-analysis of epidemiological studies available in LILACS, EMBASE, MEDLINE and Global Health. Studies were included if they followed a random sampling approach in adult population. Screening and assessment of manuscripts was carried out independently by two researchers. A random-effects meta-analysis was conducted to quantify the overall prevalence and incidence of hypertension. The Newcastle-Ottawa scale was used to assess the risk of bias in the manuscripts. RESULTS.: A total of 903 papers were screened, and only 15 were included in the estimation of hypertension prevalence, 8 in the assessment of previous hypertension diagnosis, and 4 for incidence estimations. The pooled prevalence of hypertension was 22.0% (95% CI: 20.0% - 25.0%; I2=99.2%). This estimate was lower in national studies [20.0% (95% CI: 17.0% - 22.0%; I2=99.4%] than in sub-national studies [24.0% (95% CI: 17.0% - 30.0%; I2=99.2%]. The pooled prevalence of previous hypertension diagnosis was 51.0% (95% CI: 43.0% - 59.0%; I2=99.9%). The pooled incidence of hypertension was 4.2 (95% CI: 2.0 - 6.4; I2=98.6%) per 100 person-years. The included studies did not present high risk of bias. CONCLUSIONS.: Our findings show that one in five Peruvians has hypertension, and that four new cases appear per 100 persons per year; in addition, only half of the subjects with hypertension are previously diagnosed.
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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 | 0.023 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.025 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".