Adherencia a Terapéutica y Control de la Tensión Arterial en Pacientes con Diagnóstico de Hipertensión Arterial
Bibliographic record
Abstract
Introduccion: La Hipertensión Arterial Sistemica (HAS) es el trastorno cardiovascular mas frecuente del mundo afectando al 30% de la población (Organizacion Mundial de la Salud, 2023; Mancia et al., 2023), es factor predisponente para enfermedad cardiovascular, renal y demencia (Hidalgo, 2019; Canadian Cardiovascular Society, 2020; Pérez et al., 2021; Alvarez et al., 2022; Baffour et al., 2023), la inadecuada adherencia terapéutica dificulta el control de la HAS y aumenta la probabilidad de desarrollar complicaciones (Enriquez, 2022). Objetivo: analizar la asociación entre la adherencia terapéutica farmacológica y el control de la tensión arterial de los pacientes con Hipertensión Arterial Sistémica. Material y métodos: Estudio observacional, prospectivo, transversal. Se aplicó, la Escala de Adherencia a la Medicación de Morisky de 8 ítems (MMAS-8) a 236 pacientes con diagnistico de HAS, realizado del 01 de Marzo del 2023 al 30 de Septiembre del 2023. Proyecto de investigación apegado a la declaración de Helsinki y sus enmiendas, aprobado por el Comité de Local de Ética e Investigación (R-2023-1003-007). Se utilizó Chi-cuadrada (X2) para analizar la asociación entre las variables principales, con una significancia estadística de p< 0.05, mediante el programa SPSS versión 26. Resultados: asociación entre adherencia terapéutica farmacológica y el control de la tensión arterial por Chi2 con valor de P= 0.027.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".