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Record W2053936929 · doi:10.1109/antem.2010.5552553

A preliminary assessment of EMI control policies in hospitals

2010· article· en· W2053936929 on OpenAlexaff
Mehdi Ardavan, Ketra Schmitt, C.W. Trueman

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldHealth Professions
TopicQuality and Safety in Healthcare
Canadian institutionsConcordia University
Fundersnot available
KeywordsElectromagnetic interferenceEMIWirelessTransmitterMedical equipmentComputer scienceElectromagnetic compatibilityControl (management)Risk analysis (engineering)TelecommunicationsElectrical engineeringReliability engineeringEngineeringMedicine

Abstract

fetched live from OpenAlex

The use of wireless devices around critical-care medical equipment poses the risk that the electric field strength will exceed the immunity of the equipment, potentially resulting in equipment failure and possibly harming the patient. As medical staff and visitors carrying wireless transmitters move through the hospital, there may be times when number and type of transmitters near a medical device produce an electric field that exceeds the equipment's immunity level. This paper presents a method for estimating the risk that the immunity level will be exceeded from wireless transmitters carried by medical staff throughout a 24-hour period. The method is applied to a simplified scenario of three patients in beds and twelve transmitter locations surrounding the beds, with eight medical staff on the floor. The risk of electromagnetic interference can be reduced with a suitable management policy. This paper describes a method for evaluating the usefulness of a management policy quantitatively, and then compares three policies: unrestricted use of wireless devices, restricted use, and a ban on wireless devices.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.112
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.050
GPT teacher head0.499
Teacher spread0.449 · 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.

Study designObservational
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
Published2010
Admission routes1
Has abstractyes

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