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Record W2136781649 · doi:10.1109/tbme.2005.857677

Current Density Imaging and Electrically Induced Skin Burns Under Surface Electrodes

2005· article· en· W2136781649 on OpenAlexaff
A. Patriciu, Ken Yoshida, Johannes J. Struijk, T.P. DeMonte, M.L.G. Joy, Hans Stødkilde‐Jørgensen

Bibliographic record

VenueIEEE Transactions on Biomedical Engineering · 2005
Typearticle
Languageen
FieldEngineering
TopicElectrical and Bioimpedance Tomography
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsElectrodeDuty cycleCurrent (fluid)Materials scienceCurrent densityAnodeWaveformBiomedical engineeringSkin effectOpticsVoltageMedicineElectrical engineeringChemistry

Abstract

fetched live from OpenAlex

The origin of electrical burns under gel-type surface electrodes is a controversial topic that is not well understood. To investigate the phenomenon, we have developed an excised porcine skin-gel model, and used low-frequency current density imaging (LFCDI) to determine the current density (CD) distribution through the skin before and after burns were induced by application of electrical current (200 Hz, 70% duty cycle, 20-35 mA monophasic square waveform applied to the electrodes for 30-135 min). The regions of increased CD correlate well with the gross morphological changes (burns) observed. The measurement is sensitive enough to show regions of high current densities in the pre-burn skin, that correlate with areas were burn welts were produced, thus predicting areas where burns are likely to occur. Statistics performed on 28 skin patches revealed a charge dependency of the burn areas and a relatively uniform distribution. The results do not support a thermal origin of the burns but rather electro-chemical mechanisms. We found a statistically significant difference between burn area coverage during anodic and cathodic experiments.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.582
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.006
GPT teacher head0.208
Teacher spread0.203 · 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 designBench or experimental
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

Citations31
Published2005
Admission routes1
Has abstractyes

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