Multidimensional Analysis of Hypertension in Mexico. A Complex Thinking Approach
Bibliographic record
Abstract
High blood pressure is a significant public health concern in Mexico, with a quarter of the adult population affected and a worrying lack of knowledge of the condition among patients. Adequate disease management could be better, and an integrated approach employing complex thinking is necessary to understand and effectively manage the multiple risk factors involved. The article highlights hypertension as a global problem, with a significant incidence in low- and middle-income countries, where diagnosis and awareness strategies are insufficient. Hypertension is a multifactorial disease influenced by genetic, environmental, and behavioral factors. The complex interaction of these factors requires a systematic and multidisciplinary approach, integrating complexity sciences and tools such as data mining and artificial intelligence. The study emphasizes the importance of analyzing diets and nutrition and the effects of urbanization and lifestyle changes to understand better and manage hypertension. A holistic and adaptive approach to prevention, diagnosis, and treatment can facilitate more personalized and effective intervention strategies. Finally, the article advocates for a paradigmatic change in the management of hypertension in Mexico by proposing a frame-work using complexity as the main approach. The application of complex thinking in the research and treatment of hypertension can offer more adequate solutions and improve patients' quality of life, aligning with the WHO's goals to reduce the prevalence of this condition.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".