Comparison of a Supraglottic Airway Device (v-gel®) with Blind Orotracheal Intubation in Rabbits
Bibliographic record
Abstract
Introduction Achieving a secure airway in rabbits is generally considered more difficult than in cats or dogs. Their relatively large tongue, small oropharyngeal cavity and glottis limit direct visualization. A rabbit-specific supraglottic airway device (SGAD) may offer benefits over blind orotracheal intubation. Materials & methods Fifteen adult New Zealand white rabbits were randomised to SGAD or orotracheal intubation (ETT). All animals were sedated with dexmedetomidine (0.1 mg kg-1 IM) and midazolam (0.5 mg kg-1 IM), followed by induction with alfaxalone (0.3 mg kg-1 IV). Two CT scans of the head and neck were performed, following sedation and SGAD/ETT placement. The following were recorded: time to successful device insertion, smallest cross-sectional airway area, airway sealing pressure and histological score of tracheal tissue. Data were analysed with a Mann-Whitney test. Results Two rabbits were excluded following failed ETT. Body masses were similar (ETT; n = 6, 2.6 [2.3 - 4.5] kg. SGAD; n = 7, 2.7 [2.4 - 5.0] kg). SGAD placement was significantly faster (33 [14 - 38] seconds) than ETT (59 [29-171] seconds). Cross-sectional area was significantly reduced from baseline (12.2 [6.9 - 13.4] mm2) but similar between groups (SGAD; 2.7 [2.0 - 12.3] mm2, ETT; 3.8 [2.3 - 6.6] mm2). In the SGAD group, the device tip migrated into the laryngeal vestibule in 6/7 rabbits, reducing the cross-sectional area. ETT airway seals were higher (15 [10 - 20] cmH2O), but not significantly (SGAD; 5 [5 - 20] cmH2O, p = 0.06). ETT resulted in significantly more mucosal damage (histological score 3.3 [1.0 - 5.0]), SGAD; 0.67 [0.33 - 3.67]). Conclusions The SGAD studied was faster to place and caused less damage than orotracheal intubation, but resulted in a similar cross-sectional area.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".