Modelos predictivos de complicaciones cardiovasculares de la hipertensión
Bibliographic record
Abstract
Introduction: The predictors of cardiovascular complications of hypertension can be diverse, highlighting older age, precarious social, economic and personal conditions, as well as body conditions, susceptible to modification with comprehensive health interventions. Objective: To predict cardiovascular complications of hypertension in users of a Peruvian public hospital using models. Methodology: The research was analytical, predictive, cross-sectional, with retrospective data collection. 303 patients who attended the Cajamarca Regional Hospital during 2022 were investigated. Sociodemographic data and data on cardiovascular complications of hypertension were obtained from the data stored by the hospital. Descriptive statistics included absolute and relative frequencies, confidence intervals, mean and dispersion measures, and predictive models were performed using binary logistic regression and Cohen's Kappa Index, with a statistical significance of p<0.05. Results: The most frequent cardiovascular complication was heart failure (49.5%). The model for heart failure predicts 70.6% and the years of diagnosis, occupation and marital status are included in the equation, for atrial fibrillation it predicts 82.2% and the equation includes age, sex, years of diagnosis, level of education and marital status and for angina pectoris it predicts 84.8% and the equation includes age, sex and years of diagnosis. Conclusions: The models created for the cardiovascular complications of hypertension have good predictive capacity, therefore, accurate and efficient performance in the predictor variables will allow favorable control of the complications of the disease.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".