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AD-HOC CLINICAL MONITORING OF AN LVAD USING WIRELESS BLUETOOTH TECHNOLOGY

2004· article· en· W2081808945 on OpenAlexaff
Kevin S. Holmes, Sebastian S. Szyszkowicz, Tofy Mussivand

Bibliographic record

VenueASAIO Journal · 2004
Typearticle
Languageen
FieldComputer Science
TopicBluetooth and Wireless Communication Technologies
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsBluetoothWirelessComputer scienceSoftwarePhoneEmbedded systemOperating system

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.945
Threshold uncertainty score0.586

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0030.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.064
GPT teacher head0.355
Teacher spread0.291 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2004
Admission routes1
Has abstractyes

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