AD-HOC CLINICAL MONITORING OF AN LVAD USING WIRELESS BLUETOOTH TECHNOLOGY
Bibliographic record
Abstract
Purpose: A system for wirelessly monitoring LVAD parameters in patients post-implant including after hospital discharge is not currently available. A pilot demonstration was conducted to assess the use of wireless Bluetooth technology for ad-hoc clinical monitoring. Methods: Bluetooth a short-range data radio standard was selected due to low power requirements and other factors including cost, advanced security and operation in the unlicensed Industrial-Scientific band (2.4 Ghz). A demonstration system was developed using off-the-shelf Bluetooth enabled hardware including; 2 Personal Digital Assistants (PDA) devices (Sony Clie TC-50) and a cell phone (Sony Ericsson T681). Custom software modules for the PDA were developed to allow the device to act as either a LVAD simulator or a LVAD Patient Monitor. Results: Utilizing the developed system two scenarios were successfully demonstrated. 1) In Hospital: The PDA running the LVAD monitor program was able to wirelessly locate and connect to the LVAD simulator via Bluetooth, allowing device parameters to be observed in real-time. 2) Out of Hospital: The LVAD simulator was able to generate a clinical alert (including specific device parameters) based on preset ranges (for example low flow), and automatically send this alert via a Bluetooth-enabled cell phone to a hospital based e-mail address. Conclusion: This demonstration project highlights the potential for the use of Bluetooth technology for ad-hoc monitoring of medical devices such as an LVADs. Potential benefits include enhancing patients' peace of mind and the ability to record important clinical events post hospital discharge.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".