Bibliographic record
Abstract
INTRODUCTION: Supraclavicular brachial plexus block provides consistently effective anesthesia to the upper extremity. However, traditional nerve localization techniques may be associated with a high risk of pneumothorax. In the present study, we report block success and clinical outcome data from 510 consecutive patients who received an ultrasound-guided supraclavicular block for upper extremity surgery. METHODS: After institutional review board approval, the outcome of 510 consecutive patients who received an ultrasound-guided supraclavicular block for upper extremity surgery was reviewed. Real-time ultrasound guidance was used with a high-frequency linear probe. The neurovascular structures were imaged on short axis, and the needle was inserted using an in-plane technique with either a medial-to-lateral or lateral-to-medial orientation. RESULTS: Five hundred ten ultrasound-guided supraclavicular blocks were performed (50 inpatients, 460 outpatients) by 47 different operators at different levels of training over a 24-month period. Successful surgical anesthesia was achieved in 94.6% of patients after a single attempt; 2.8% required local anesthetic supplementation of a single peripheral nerve territory; and 2.6% received an unplanned general anesthetic. No cases of clinically symptomatic pneumothorax developed. Complications included symptomatic hemidiaphragmatic paresis (1%), Horner syndrome (1%), unintended vascular punctures (0.4%), and transient sensory deficits (0.4%). CONCLUSIONS: Ultrasound-guided supraclavicular block is associated with a high rate of successful surgical anesthesia and a low rate of complications and thus may be a safe alternative for both inpatients and outpatients. Severe underlying respiratory disease and coagulopathy should remain a contraindication for this brachial plexus approach.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".