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Record W2069158600 · doi:10.1109/temc.2014.2362717

EMI Risk Assessment in a Hospital Ward With One and Two Roaming Wireless Transmitters

2014· article· en· W2069158600 on OpenAlexafffund
Mehdi Ardavan, C.W. Trueman, Ketra Schmitt

Bibliographic record

VenueIEEE Transactions on Electromagnetic Compatibility · 2014
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Compatibility and Measurements
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsEMITransmitterRoamingElectromagnetic interferenceWirelessInterference (communication)Computer scienceElectromagnetic compatibilityElectrical engineeringTelecommunicationsRisk analysis (engineering)EngineeringReliability engineeringMedicine

Abstract

fetched live from OpenAlex

To control electromagnetic interference, hospitals often specify that mobile transmitters may not be brought any closer than a minimum separation distance (MSD) to an electronic medical device. This paper investigates the risk that the field strength due to mobile transmitters exceeds the immunity level of a medical device. The spatial variation of the field strength can be characterized by the well-known Ricean probability distribution, using the Sabine method to evaluate the parameters. The mobility of transmitters is accounted for by assuming a function for the probability that a transmitter is present at each location throughout the hospital room. The risk of exceeding immunity is estimated with no restriction on the movement of mobile transmitters, and the reduction in risk is estimated when an MSD policy is enforced. Staff may not fully comply with the MSD, so the increase in risk with specified levels of non-compliance is found. It is shown that with some non-compliance the risk of exceeding immunity becomes constant with increasing MSD, and so specifying a larger MSD does not necessarily increase safety.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
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 head0.006
GPT teacher head0.204
Teacher spread0.198 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations10
Published2014
Admission routes2
Has abstractyes

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