Does the Sedative Agent Facilitate Emergency Rapid Sequence Intubation?
Bibliographic record
Abstract
OBJECTIVES: To ascertain whether the sedative agent administered during neuromuscular-blocking agent-facilitated intubation (rapid sequence intubation [RSI]) influences the number of attempts and overall success at RSI. METHODS: Records were drawn from an ongoing, prospective multicenter registry of emergency department intubations. Conditional logistic regression stratified by institution was used to identify factors associated with multiple intubation attempts and unsuccessful RSI. RESULTS: Of 3,407 intubations over 33 months in 22 institutions, 2,380 involved RSI. After correcting for the specialty and experience of the intubator and for the presence of airway aberrancy, the sedative agent was significantly associated with the number of attempts at intubation (p = 0.002). Specifically, the use of etomidate (adjusted odds ratio [OR] 0.35 [95% CI = 0.17 to 0.72]), ketamine (OR 0.27 [95% CI = 0.11 to 0.65]), a benzodiazepine (OR 0.47 [95% CI = 0.23 to 0.95]), or no sedative agent (OR 0.51 [95% CI = 0.23 to 1.13]) prior to neuromuscular blockade was associated with a lower likelihood of successful intubation on the first attempt, as compared with thiopental, methohexital, or propofol. The adjusted odds ratios for the likelihood of overall success had similar point estimates, but did not reach statistical significance due to lack of power (p = 0.2, with 36 unsuccessful intubations). Among patients receiving etomidate, intubation was more likely to be successful on the first attempt with increasing doses of either etomidate or succinylcholine. CONCLUSIONS: Thiopental, methohexital, and propofol appear to facilitate RSI in emergency department patients, independent of patient characteristics or intubator training. A deeper plane of anesthesia may improve intubating conditions in emergency patients undergoing RSI by complementing incomplete muscle paralysis.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.070 | 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 teacher head, 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".