Noninvasive ventilation in cats
Bibliographic record
Abstract
OBJECTIVE: The primary objective of this study was to assess the feasibility of noninvasive mechanical ventilation (NIV) in cats. The secondary objective was to determine whether cardiovascular parameters and anesthetic drug requirements associated with noninvasive ventilation differ from those associated with invasive ventilation. DESIGN: Randomized, cross-over design. SETTING: A research laboratory in a veterinary teaching hospital. ANIMALS: Eight healthy adult cats, 3 intact females and 5 intact males, weighing between 3 and 6 kg, were used. INTERVENTIONS: Each cat was randomly assigned to NIV via nasal mask, or invasive ventilation using an endotracheal tube. Mechanical ventilation was performed for 6 hours. Anesthesia was provided using continuous infusions of propofol and butorphanol. After a minimum 9-day washout period, the procedure was repeated using the alternate ventilation interface. MEASUREMENTS AND MAIN RESULTS: Cardiovascular parameters (heart rate, rectal temperature, direct arterial blood pressure), arterial blood gases, drug requirements, sedation score, and ventilation parameters, were monitored throughout the procedures. These values were evaluated using ANCOVA for repeated measures. All cats were effectively ventilated using NIV. There were no significant differences in cardiovascular parameters, drug requirements, or sedation scores between groups. Although PaCO(2) values did not differ, PaO(2) values were significantly higher in the invasively ventilated group. Inspiratory tidal volumes were similar between groups, whereas expiratory tidal volumes were significantly lower in the NIV group. Inspiratory pressures were significantly higher in the NIV group. Respiratory frequency was significantly higher in the invasively ventilated group. CONCLUSIONS: NIV of cats is possible. However, currently it does not confer any cardiovascular benefit over invasive ventilation and drug requirements are similar. Use of a correctly fitted mask is essential for successful NIV as air leaks account for the observed discrepancy between inspiratory and expiratory volumes. Further investigation into this modality is warranted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".