Randomized Comparison of the Single-Injection Volar Subcutaneous Block and the Two-Injection Dorsal Block for Digital Anesthesia
Bibliographic record
Abstract
BACKGROUND: Two commonly used methods of digital nerve block with local anesthetic are the two-injection dorsal technique and the single-injection volar subcutaneous technique. The authors compared these two digital block techniques with respect to local anesthetic injection pain and recipient preference of anesthetic technique. METHODS: Twenty-seven volunteers had the long finger of each hand injected with 2% lidocaine with 1:100,000 epinephrine. The two-injection dorsal method was used on one long finger and the other long finger received the volar single-injection technique. Volunteers completed a pain scale for each block and were then asked which technique they would prefer. The area of anesthetic skin was assessed in each finger by pinprick testing, and photographs were taken. RESULTS: Although there was a lower pain score for the volar single-injection block, the difference in pain scores between the two techniques was not statistically significant. However, 22 of the 27 subjects indicated that they would select the volar over the dorsal block if a future block was required, and this preference for the volar block was statistically significant. CONCLUSIONS: Although the difference in pain scores between the two techniques was not statistically significant, volunteers who received both blocks would prefer the volar single-injection subcutaneous block if given a choice. Therefore, the single-injection volar subcutaneous block is recommended as the technique of choice for anesthesia of the digit, except in patients for whom anesthesia over the dorsum of the proximal phalanx is required. These patients may prefer a supplementary dorsal nerve block or a traditional two-injection block.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".