Combined Versus Sequential Injection of Mepivacaine and Ropivacaine for Supraclavicular Nerve Blocks
Bibliographic record
Abstract
BACKGROUND: An ideal local anesthetic with rapid onset and prolonged duration has yet to be developed. Clinicians use mixtures of local anesthetics in an attempt to combine their advantages. We tested the hypothesis that sequential supraclavicular injection of 1.5% mepivacaine followed 90 secs later by 0.5% ropivacaine speeds onset of sensory block and prolongs duration of analgesia compared with simultaneous injection of the same 2 local anesthetics. METHODS: We enrolled 103 patients undergoing surgery suitable for supraclavicular anesthesia. The primary outcome was time to 4-nerve sensory block onset in each of the 4 major nerve distributions: median, ulnar, radial, and musculocutaneous. Secondary outcomes included time to onset of first sensory block, time to complete motor block, duration of analgesia, pain scores at rest and with movement, and total opioid consumption. Outcomes were compared using the Kaplan-Meier analysis with the log-rank test or the analysis of variance, as appropriate. RESULTS: Times to 4-nerve sensory block onset were not different between sequential and combined anesthetic administration. The time to complete motor block onset was faster in the combined group as compared with the sequential. There were not significant differences between the 2 randomized groups in other secondary outcomes, such as the time to onset of first sensory block, the duration of analgesia, the pain scores at rest or with movement, or the total opioid consumption. CONCLUSIONS: Sequential injection of 1.5% mepivacaine followed 90 secs later by 0.5% ropivacaine provides no advantage compared with simultaneous injection of the same doses.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".