Adherencia a Terapéutica y Control de la Tensión Arterial en Pacientes con Diagnóstico de Hipertensión Arterial
Bibliographic record
Abstract
Introduction: Systemic Arterial Hypertension (HTA) is the most common cardiovascular disorder in the world, affecting 30% of the population (Organizacion Mundial de la Salud, 2023; Mancia et al., 2023), it is a predisposing factor for cardiovascular, kidney disease and dementia (Hidalgo, 2019; Canadian Cardiovascular Society, 2020; Pérez et al., 2021; Alvarez et al., 2022; Baffour et al., 2023), inadequate therapeutic adherence difficult the control of HTA and increases the probability of developing related complications (Enriquez, 2022). Objective: to analyze the association between pharmacological therapeutic adherence and blood pressure control in patients with Systemic Arterial Hypertension. Material and methods: Observational, prospective, cross-sectional study. The 8-item Morisky Medication Adherence Scale (MMAS-8) was applied to 236 patients with SAH diagnosis, carried out from March 1, 2023 to September 30, 2023. Research project was developed according to Helsinki declaration and its amendments, approved by the Local Ethics and Research Committee (R-2023-1003-007). Chi-square (X2) was used to analyze the association between the main variables, with a statistical significance of p < 0.05, using the SPSS 26 version program. Results: statistically significative association between pharmacological therapeutic adherence and blood pressure control by X2 with P value= 0.027.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".