An approach for modeling of a medical equipment for the estimation of leakage currents
Bibliographic record
Abstract
In an operating theatre, electro medical equipment (EME) may fail and cause health hazards as the passing of a weak but hazardous current (leakage current) through the heart of the patient during surgical interventions.This occurs evidently during their use, when connected to the patient.The related values of leakage currents can be estimated by an electrical circuit model.To obtain the circuit of a surgical layout, the electrical models of medical location supply power system, human body and EME involved in a determined surgical intervention ought to be drawn.The present work focuses on the method to obtain the model of EME.The idea is to model by circuits each leakage current measurement set-up performed in accordance with the international standard IEC 60601-1.To assign the values to the model parameters, it is necessary to obtain also some information on the EME as the values of insulation impedances and the feasible leakage current paths inside it.The case of a commercial defibrillator is taken as an example to show the feasibility of the method.The comparison between the leakage currents simulated by the circuit and the ones measured is here presented.The agreement is satisfactory.An estimation of the model sensitivity due to the uncertainty in the knowledge of the parameters has been performed too, by using the Monte Carlo method.The extension of this approach to draw the model of other EME is also considered in view of the realization of a surgical layout circuit.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".