A Practical Guide to Local Anesthetic Options for Wide-awake Hand Surgery (WALANT): A Laboratory-based Experimental Study
Bibliographic record
Abstract
Summary: Ongoing supply chain issues are impacting local anesthetic availability, limiting choices for hand surgery under local anesthesia (wide-awake local anesthesia no tourniquet [WALANT] hand surgery). WALANT can be safely performed using multiple local anesthesia preparations; however, epinephrine (for hemostasis) and sodium bicarbonate (NaHCO3) (to reduce injection pain) are often added to create the desired preparation. Here, we tested how much NaHCO3 to add to various local anesthetic to approximate body pH. Five samples of each commercially available local anesthetics were tested for pH including 1% lidocaine, 2% lidocaine, 1% lidocaine with 1/200,000 epinephrine, 0.25% bupivacaine, 0.5% bupivacaine, 0.5% bupivacaine with 1/100,000 epinephrine, 1% mepivacaine, 2% mepivacaine, and ropivacaine, with or without premixed or exogenous epinephrine. Each sample was buffered with NaHCO3, with volumes increasing by 0.1 mL until target pH was measured or visible precipitate occurred; 1.0 mL of 8.4% bicarbonate can be added to 10 mL of 1% lidocaine with 1/100,000 exogenous epinephrine to yield pH 7.3. Similarly, with 10 mL 1% lidocaine with premixed 1/200,000 epinephrine, the ideal volume of buffer remains 1.0 mL. Buffering bupivacaine created a solid precipitate in all samples when more than 0.2 mL of NaHCO3 per 10 mL of sample was added. Ropivacaine precipitated with all volumes of buffer. Mepivacaine can be buffered using 0.3 mL NaHCO3 per 10 mL to achieve physiological pH, without precipitate. In conclusion, we present guidance for surgeons to mix local anesthetic, epinephrine, and NaHCO3 to optimize pH for comfortable injection of local anesthesia in WALANT procedures.
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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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.044 | 0.021 |
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".