La Iniciativa HEARTS en Cuba: experiencias tras 5 años de implementación
Bibliographic record
Abstract
In order to describe the changes in the control of arterial hypertension from 2016 to 2021 and the relationship between the progress made and the maturity of implementation of the HEARTS Initiative at the first level of care in Cuba, a prospective implementation study was designed to promote the correct measurement of blood pressure with validated automatic sphygmomanometers, risk-based care, introduction of standardized antihypertensive treatment protocols, secondary prevention, and teamwork. Patient education, active case-finding, and community activities were also encouraged. Data were obtained from implementation logs and were analyzed with SPSS Statistics V21; outcome values and 95% confidence intervals were reported. Population indicators were evaluated: registration coverage, control between treatments, population control, and the HEARTS in the Americas Maturity Index. The number of participating centers increased from a single demonstration center in 2016 to 22 polyclinics in six provinces and seven municipalities in 2021. There was a significant increase in the absolute values for the total population served, number of hypertensive patients registered, controlled patients among those treated, and controlled hypertensive patients among all adults, although with annual variations in percentages of coverage, control between treatments, and population control. The greatest progress occurred in centers with high-performance health teams. Five years after implementation of the HEARTS Initiative in Cuba, the methodology is becoming institutionalized.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".