The Role of Telemedicine in Improving Hypertension Management Outcomes: A Systematic Review
Bibliographic record
Abstract
Telehealth has been proven to be effective in a variety of healthcare settings and has enhanced patient utilization of healthcare services. Little is known about the use of telehealth in the treatment of hypertension. This study aimed to categorize and identify data related to various telehealth technologies and intervention types used in the management of hypertension. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were used to search the literature based on predetermined inclusion and exclusion criteria. These databases contained 1,483 relevant articles, which were screened for duplication using Endnote software. After a careful full-text article evaluation, only 42 of these articles were found to be relevant. The Newcastle-Ottawa Scale was used to assess the risk of bias in each included study. The majority of studies (23.8%) were conducted in urban areas (33.3%), were from the United States, and used a quantitative study approach (69%), according to the proportions of studies displaying different patterns over the past 10 years. Telemonitoring and teleconsultation are the two most used telehealth techniques for managing hypertension. Asynchronous telehealth is quickly becoming the most popular technique for controlling hypertension. In hypertension management, telehealth refers to the use of communication technologies to remotely monitor and regulate blood pressure as well as offer medical advice and counseling.
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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 | 0.010 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".