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Record W2106110374 · doi:10.1109/ds-rt.2008.50

Agent-Based Mobile Middleware Architecture (AMMA) for Patient-Care Clinical Data Messaging Using Wireless Networks

2008· article· en· W2106110374 on OpenAlexafffund
Bhuvaneswari Arunachalan, Janet Light

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicDistributed systems and fault tolerance
Canadian institutionsInnovatia (Canada)
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceAsynchronous communicationMiddleware (distributed applications)Message brokerComputer networkProtocol (science)Mobile computingContext (archaeology)Short Message ServiceDistributed computing

Abstract

fetched live from OpenAlex

Mobile messaging in healthcare environment is asynchronous based on real-time events, set of contextual elements such as location of service, resource availability, and guaranteed message delivery. Due to the critical nature of the healthcare delivery system, the mobile messaging has two key issues: reliability of message passing and synchronization of message delivery. AMMA provides solution for reliable asynchronous message passing by implementing an event-based context-centric agent communication protocol, and Mobile Message Passing Protocol with synchronized message delivery using global checkpoint method. In this demo the AMMA, a mobile agent system for reliable clinical data mobile messaging is presented. HL7 clinical document architecture is used for defining agents. AMMA is designed as part of the electronic patient call report (e-PCR) project for the 911- emergency medical services. The demo also includes the e-PCR data capturing tool, the wireless communication protocol and the middleware.

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.892
Threshold uncertainty score0.869

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.000
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 head0.090
GPT teacher head0.329
Teacher spread0.239 · 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 designSimulation or modeling
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

Citations9
Published2008
Admission routes2
Has abstractyes

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