How Much Volume of Local Anesthesia and How Long Should You Wait After Injection for an Effective Wrist Median Nerve Block?
Bibliographic record
Abstract
BACKGROUND: Many surgeons and emergentologists use non-ultrasound-guided wrist nerve blocks. There is little evidence to guide the ideal volume of local anesthesia or how long we should wait after injection before performing pain-free procedures. This pilot study examined time to maximal anesthesia to painful needle stick in 14 volunteer participants receiving bilateral wrist blocks of 6 versus 11 mL of local. METHODS: One surgeon performed all 14 bilateral wrist median nerve blocks in participants who remained blinded until after bandages were applied to their wrist. No one could see which wrist received the larger 11-mL volume injection versus the 6-mL block. Blinded sensory assessors then measured perceived maximal numbness time and numbness to needle stick pain in the fingertips of the median nerve distribution. RESULTS: Failure to get a complete median nerve block occurred in seven of fourteen 6-mL wrist blocks versus failure in only one of fourteen 11-mL blocks. Perceived maximal numbness occurred at roughly 40 minutes after injection, but actual numbness to painful needle stick took around 100 minutes. CONCLUSIONS: Incomplete median nerve numbness occurred with both 6- and 11-mL non-ultrasound-guided blocks at the wrist. In those with complete blocks, it took a surprisingly long time of 100 minutes for maximal anesthesia to occur to painful needle stick stimuli to the fingertips of the median nerve distribution. Non-ultrasound-guided median nerve blocks at the wrist as described in this article lack reliability and take too long to work.
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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 | 0.004 | 0.015 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".