Ultrasound-Guided Infraclavicular Versus Supraclavicular Block
Bibliographic record
Abstract
In this prospective study we compared ultrasound-guided (USG) infraclavicular and supraclavicular blocks for performance time and quality of block. We hypothesized that the infraclavicular approach would result in shorter performance times with a quality of block similar to that of the supraclavicular approach. Eighty patients were randomized into two equal groups: Group I (infraclavicular) and Group S (supraclavicular). All blocks were performed using ultrasound visualization with a 7.5-MHz linear probe and neurostimulation. The anesthetic mixture consisted of 0.5 mL/kg of bupivacaine 0.5% and lidocaine hydrocarbonate 2% (1:3 vol.) with epinephrine 1:200,000. Sensory block, motor block, and supplementation rates were evaluated for the musculocutaneous, median, radial, and ulnar nerves. Surgical anesthesia without supplementation was achieved in 80% of patients in group I compared with 87% in Group S (P = 0.39). Supplementation rates were significantly different only for the radial territory: 18% in Group I versus 0% in group S (P = 0.006). Block performance times were not different between groups (4.0 min in Group I versus 4.65 min in Group S; P = 0.43). Technique-related pain scores were not different between groups (I: 2.0; S: 2.0; P = 1.00). We conclude that USG infraclavicular block is at least as rapidly executed as USG supraclavicular block and produces a similar degree of surgical anesthesia without supplementation.
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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 | 0.002 |
| 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) | 0.001 | 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".