Evaluation of the effects of helmet continuous positive airway pressure on laryngeal size in dogs anesthetized with propofol and fentanyl using computed tomography
Bibliographic record
Abstract
Abstract Objective To evaluate the effect of 5 cm H2O of continuous positive airway pressure (CPAP) on laryngeal size in spontaneously breathing anesthetized dogs via computed tomography (CT). Design Prospective, randomized, cross‐over clinical study. Setting University teaching hospital and referral private practice. Animals Eight healthy client‐owned dogs undergoing CT. Interventions Dogs were sedated with acepromazine 20 μg/kg IM and induced with fentanyl 2 μg/kg and propofol 3–5 mg/kg IV before being maintained on fentanyl (5 μg/kg/h) and propofol (0.3 mg/kg/min) constant rate infusion. Dogs received an air/oxygen mixture with (CPAP) and without (NO‐CPAP) 5 cm H2O of CPAP in a random order. Each study step lasted 15 minutes. Measurements and Main Results Ten minutes after the beginning of each study period, a CT scan of the laryngeal region was obtained at end‐expiration. CT images were analyzed to determine the laryngeal cross‐sectional area (CSA; cm2), total volume (VTOT; cm3), and laterolateral and dorsoventral diameters (DLL and DDV, respectively; cm). Differences between the 2 treatments were analyzed with t‐test for paired data (P < 0.05). Compared to the NO‐CPAP, during CPAP the CSA increased by 53.3 ± 23.1% (ie, from 3.3 ± 0.8 to 5.1 ± 1.3 cm2, P = 0.0004), VTOT increased by 52.4 ± 13.6% (from 6.2 ± 1.7 to 9.4 ± 2.4 cm3, P < 0.0001), and DLL and DDV were 55.5 ± 13.3% (3.6 ± 0.8 vs 2.4 ± 0.5 cm, P = 0.006) and 20.3 ± 8.8% larger (3.2 ± 0.7 vs 2.7 ± 0.6 cm, P = 0.0002), respectively. Conclusions Laryngeal volume and cross sectional area increased during the application of 5 cm H2O of helmet CPAP in spontaneously breathing anesthetized dogs.
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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 | 0.000 | 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) | 0.001 | 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".