STUDY OF THE RISK FACTORS DETECTED IN THE EVOLUTION OF PATIENTS DIAGNOSED WITH HYPERTENSION
Bibliographic record
Abstract
Cardiovascular diseases are part of a group of conditions starting from the heart structures and blood vessels. Since the symptomatology of these conditions is complex, which affects the whole body, special attention is required from the medical personnel. The management of these pathologies is a complex one, which obligatorily implies the existence of good communication between the primary healthcare as well as the treating specialists.According to the latest statistical studies, cardiovascular diseases are currently the main cause of death worldwide. According to statistics from 2015, 17.9 million deaths were due to cardiovascular pathologies, which is 6.3% more than the death rate in the 90s. From the point of view of the distribution of these conditions depending on sex, the predominance is noted among men of acute coronary diseases and vascular accidents, the same conditions being found also in the case of the opposite sex [McGill, H. 2008].Arterial hypertension is defined by specialized literature as a chronic cardiovascular disease. It is characterized by the constant presence of elevated blood pressure values. Over time, this conjuncture creates an environment in which it is stimulated in the development of coronary diseases, aneurysms, but also peripheral vascular disease, as well as purely cardiac pathologies such as heart failure (which has systemic symptoms), or atrial fibrillation [Lawes, C, 2001].
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".