NOISE AND ARTERIAL HYPERTENSION. A NOISY RISK FACTOR FOR THE SILENT KILLER DISEASE
Bibliographic record
Abstract
Objective: In recent decades, anthropogenic activities, including urbanization, construction, industry, agriculture, deforestation, transportation, and the building of dams, have significantly impacted ecosystems and contributed to environmental degradation. Specifically, noise, ambient temperature and humidity that are often referred as forgotten pollutants have gained increasing recognition as critical factors affecting environmental quality and public health. However, it remains unclear whether prolonged exposure to these factors increases the hypertension (HTN) risk. The aim of the present study was to examine the correlation of noise, air temperature and atmospheric humidity with blood pressure (BP) among patients without HTN.Design and method: A total of 40 patients (20 men and 20 women) underwent 24-hour ambulatory BP monitoring (ABPM). Environmental noise, ambient temperature and humidity were recorded using an external standardized portable device. Results: ABPM readings showed that 24-hour systolic BP was 131.5±14.8 mmHg, 24-hour diastolic BP was 80.5±11.0 mmHg and 24-hour mean BP was 94.8±11.2 mmHg. According to the recording device, the participants were exposed to 51.6±5.1 dBA of noise, 32.5±3.9oC of temperature and 44.2±7.1% of humidity. Statistical analysis revealed a moderate positive correlation between noise and BP. Nonetheless, a weak negative correlation was observed between ambient temperature and humidity with BP. Conclusions: The current study demonstrated a moderate positive correlation between noise and BP suggesting that noise might be a causative factor of arterial HTN. In addition, a weak negative correlation was identified between ambient temperature and humidity with BP. These findings highlight the potential role of environmental factors, particularly noise, as modifiable risk factors for HTN. Future public health policies should consider noise reduction and climate adaptation measures to improve cardiovascular outcomes and enhance the quality of life.
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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 | 0.000 | 0.000 |
| 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) | 0.003 | 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".