Electrical impedance tomography to measure lung ventilation distribution in healthy horses and horses with left-sided cardiac volume overload
Bibliographic record
Abstract
Abstract Background Left-sided cardiac volume overload (LCVO) can cause fluid accumulation in lung tissue changing the distribution of ventilation, which can be evaluated by electrical impedance tomography (EIT). Objectives To describe and compare EIT variables in horses with naturally occurring compensated and decompensated LCVO and compare them to a healthy cohort. Animals Fourteen adult horses, including university teaching horses and clinical cases (healthy: 8; LCVO: 4 compensated, 2 decompensated). Methods In this prospective cohort study, EIT was used in standing, unsedated horses and analyzed for conventional variables, ventilated right (VAR) and left (VAL) lung area, linear-plane distribution variables (avg-max VΔZLine, VΔZLine), global peak flows, inhomogeneity factor, and estimated tidal volume. Horses with decompensated LCVO were assessed before and after administration of furosemide. Variables for healthy and LCVO-affected horses were compared using a Mann-Whitney test or unpaired t-test and observations from compensated and decompensated horses are reported. Results Compared to the healthy horses, the LCVO cohort had significantly less VAL (mean difference 3.02; 95% confidence interval .77-5.2; P = .02), more VAR (−1.13; −2.18 to −.08; P = .04), smaller avg-max VΔZLLine (2.54; 1.07-4.00; P = .003) and VΔZLLine (median difference 5.40; 1.71-9.09; P = .01). Observation of EIT alterations were reflected by clinical signs in horses with decompensated LCVO and after administration of furosemide. Conclusions and Clinical Importance EIT measurements of ventilation distribution showed less ventilation in the left lung of horses with LCVO and might be useful as an objective assessment of the ventilation effects of cardiogenic pulmonary disease in horses.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".