Are Intubation Conditions Using Rocuronium Equivalent to Those Using Succinylcholine?
Bibliographic record
Abstract
Abstract Objective: To determine whether the intubation conditions created by rocuronium are equivalent to those of succinylcholine during rapid‐sequence induction (RSI). Methods: Medline, EMBASE, and the Cochrane Controlled Trials Register were searched for randomized clinical trials (RCTs). The search strategy included all generic and trade names for succinylcholine and rocuronium, anesthesia, neuromuscular blockade, and a validated RCT filter. Intubation conditions were a required outcome. Two reviewers assessed studies for eligibility, data extraction, and quality. Intubation conditions were scored with Goldberg's scale (excellent conditions defined as clear vocal cords, easy tube insertion, and no cough). A‐priori subgroup analysis was conducted for the sedative, use of opioids, true versus modified RSI, age group, and the dose of rocuronium. Data were analyzed with Metaview 4.1 for relative risk (RR) of achieving excellent intubation conditions. A sample size calculation determined that n = 468 is required for equivalence. Results: Forty articles were identified; ten articles were excluded by the inclusion criteria, two were duplicate publications, and two had insufficient data. Therefore, 26 studies were analyzed. Overall, rocuronium was inferior to succinylcholine, with a RR = 0.87 (95% CI = 0.81 to 0.94) (N = 1,606). However, intubation conditions were similar in the propofol subgroup, with a RR = 0.96 (95% CI = 0.87 to 1.06) (N = 640). Failed intubations (N = 28) were equivalent in the two groups. Conclusions: Overall, succinylcholine creates excellent intubation conditions more reliably than rocuronium. If a second‐line agent is required, rocuronium used with propofol creates intubation conditions equivalent to those with succinylcholine.
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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 | 0.022 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.018 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".