Biobeat monitor utilization in various healthcare settings: A systematic review
Bibliographic record
Abstract
Background: Ensuring accurate and continuous monitoring of patients' physiological parameters is paramount for evaluating their health status and guiding clinical decision-making. Technological advancements have the potential to significantly improve patient care and outcomes by offering a seamless continuum of healthcare experiences. Biobeat Technologies Ltd has pioneered a non-invasive wearable approach to acquiring advanced hemodynamic parameters, employing devices such as the BB-613WP wrist monitor and the BB-613P chest patch. Biobeat devices have been applicable across many clinical settings, as substantiated by a growing body of research. This systematic review endeavours to comprehensively consolidate the evidence regarding using Biobeat monitors across various clinical scenarios. Methods: From 2016 to 2024, a thorough literature search was conducted across multiple databases. The inclusion criteria for selected studies comprised adult patients aged 18 years or older in any healthcare setting, employing Biobeat monitoring devices (wrist monitors and/or chest patches), reporting at least one outcome or finding, and presenting fully published original research studies, including randomized controlled trials and prospective or retrospective cohort studies. The quality and risk of bias assessment for the studies was performed using the Newcastle-Ottawa scale and COSMIN scoring system. Results: Among 27 studies identified, 15 met the inclusion criteria, involving 4248 patients. These included 14 prospective observational studies and one retrospective cohort study; no randomized control trials were identified. Notably, eight studies were conducted in ambulatory settings, with 1 study focusing on patients undergoing labor and delivery. Additionally, three studies were carried out in general inpatient wards, 1 in a medical ICU and another in a cardiac surgery ICU (CSICU). Furthermore, 1 study presented results from 3 separate investigations- 2 in ambulatory settings and 1 in the CSICU. Across all studies, Biobeat devices were consistently utilized, with each study reporting positive outcomes associated with their use. Conclusion: This systematic review demonstrates that Biobeat's non-invasive wearable devices have been effectively utilized across various clinical settings, consistently contributing to positive patient outcomes. The versatility and reliability of these devices highlight their potential to enhance patient care and support clinical decision-making, warranting further research to explore their broader applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".