Performance evaluation of electrode design and material for a large animal electrical impedance tomography belt
Bibliographic record
Abstract
Abstract Background Electrical impedance tomography (EIT) produces lung ventilation images via a thoracic electrode belt. Robust electrode design and material, providing low electrode skin contact impedance (SCI), is needed in veterinary medicine. The aim of this study was to compare three EIT electrode designs and materials. Methods Simulations of cylindrical, rectangular and spiked electrode designs were used to evaluate electrode SCI as a function of electrode size, where skin contact was uneven. Gold‐plated washers ( E GW ), zinc‐plated rivets ( E ZR ) and zinc‐galvanised spikes ( E ZS ) were assigned randomly on two interconnected EIT belts. Gel was applied to the cranial or caudal belt and placed on 17 standing cattle. SCI was recorded at baseline and 3, 5, 7, 9 and 11 minutes later. Results Simulations that involved electrodes with a greater skin contact area had lower and more uniform SCI. In cattle, SCI decreased with all electrodes over time ( p < 0.01). Without gel, no difference was found between E GW and E ZS , while SCI was higher for E ZR ( p < 0.03). With gel, SCI was lower in E GW and E ZR ( p < 0.026), with the SCI in E GW being the lowest ( p < 0.01). Limitations Low numbers of animals and static electrode position may affect SCI. Conclusions Electrode design is important for EIT measurement, with larger electrode designs able to compensate for the use of less conductive materials. Gel is not necessary to achieve acceptable SCI in large animals.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".