Intubation following high-dose rocuronium in a cat with protracted laryngospasm
Bibliographic record
Abstract
CASE SUMMARY: An 11-year-old spayed female domestic shorthair cat with a history of laryngospasm at induction of general anesthesia presented for dental evaluation and treatment. The cat was premedicated with hydromorphone (0.05 mg/kg) and alfaxalone (0.5 mg/kg) intravenously, pre-oxygenated for 5 mins (3 l/min, face mask) and anesthesia was induced with alfaxalone (to effect) intravenously. Lidocaine (0.1 ml, 2%) was applied topically to the arytenoid cartilages following loss of jaw tone. Laryngospasm was not noted during or immediately following lidocaine application. However, after waiting 60 s for the onset of effect of the topical lidocaine, laryngospasm was apparent. Orotracheal intubation by direct visualization was unsuccessful after four attempts by three anesthetists (with increasing levels of experience). At this point, a failed intubation was declared and the non-depolarising neuromuscular blocking agent rocuronium (1 mg/kg IV) given, resulting in arytenoid abduction and appropriate conditions for intubation. Successful intubation occurred 9 mins after induction of anesthesia. Oxygen was continuously supplemented throughout and arterial hemoglobin saturation with oxygen was never <94%. RELEVANCE AND NOVEL INFORMATION: To the authors' knowledge, this is the first report of the use of high-dose rocuronium to successfully resolve prolonged laryngospasm at induction of general anesthesia in a cat. Despite laryngospasm and a delay in achieving orotracheal intubation, low values for arterial hemoglobin saturation with oxygen (indicative of hypoxemia) were not observed, highlighting the benefits of pre-oxygenation and apneic oxygenation. The principles of the Difficult Airway Society 2015 guidelines were followed in managing this difficult intubation.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".