Can bronchoconstriction and bronchodilatation in horses be detected using electrical impedance tomography?
Bibliographic record
Abstract
Abstract Background Electrical impedance tomography (EIT) generates images of the lungs based on impedance change and was able to detect changes in airflow after histamine challenge in horses. Objectives To confirm that EIT can detect histamine-provoked changes in airflow and subsequent drug-induced bronchodilatation. Novel EIT flow variables were developed and examined for changes in airflow. Methods Bronchoconstriction was induced using stepwise histamine bronchoprovocation in 17 healthy sedated horses. The EIT variables were recorded at baseline, after saline nebulization (control), at the histamine concentration causing bronchoconstriction (Cmax) and 2 and 10 minutes after albuterol (salbutamol) administration. Peak global inspiratory (PIFEIT) and peak expiratory EIT (PEFEIT) flow, slope of the global expiratory flow-volume curve (FVslope), steepest FVslope over all pixels in the lung field, total impedance change (surrogate for tidal volume; VTEIT) and intercept on the expiratory FV curve normalized to VTEIT (FVintercept/VTEIT) were indexed to baseline and analyzed for a difference from the control, at Cmax, 2 and 10 minutes after albuterol. Multiple linear regression explored the explanation of the variance of Δflow, a validated variable to evaluate bronchoconstriction using all EIT variables. Results At Cmax, PIFEIT, PEFEIT, and FVslope significantly increased whereas FVintercept/VT decreased. All variables returned to baseline 10 minutes after albuterol. The VTEIT did not change. Multivariable investigation suggested 51% of Δflow variance was explained by a combination of PIFEIT and PEFEIT. Conclusions and Clinical Importance Changes in airflow during histamine challenge and subsequent albuterol administration could be detected by various EIT flow volume variables.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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".