Assessment of distribution of ventilation by electrical impedance tomography in standing horses
Bibliographic record
Abstract
The aim was to evaluate the feasibility of using electrical impedance tomography (EIT) in horses. Thoracic EIT was used in nine horses. Thoracic and abdominal circumference changes were also measured with respiratory ultrasound plethysmography (RUP). Data were recorded during baseline, rebreathing of CO2 and sedation. Three breaths were selected for analysis from each recording. During baseline breathing, horses regularly took single large breaths (sighs), which were also analysed. Functional EIT images were created using standard deviations (SD) of pixel signals and correlation coefficients (R) of each pixel signal with a reference respiratory signal. Left-to-right ratio, centre-of-ventilation and global-inhomogeneity-index were calculated. RM-ANOVA and Bonferroni tests were used (P < 0.05). Distribution of ventilation shifted towards right during sighs and towards dependent regions during sighs, rebreathing and sedation. Global-inhomogeneity-index did not change for SD but increased for R images during sedation. The sum of SDs for the respiratory EIT signals correlated well with thoracic (r(2) = 0.78) and abdominal (r(2) = 0.82) tidal circumferential changes. Inverse respiratory signals were identified on the images at sternal location and based on reviewing CT images, seemed to correspond to location of gas filled intestines. Application of EIT in standing non-sedated horses is feasible. EIT images may provide physiologically useful information even in situations, such as sighs, that cannot easily be tested by other methods.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".