How acidic is the lidocaine we are injecting, and how much bicarbonate should we add?
Bibliographic record
Abstract
BACKGROUND: The infiltration of local anesthetics can be painful, which is likely due, in part, to their acidity. In spite of a Cochrane study that recommended neutralizing lidocaine with bicarbonate to decrease the pain of injection, not many surgeons have adopted the practice, and there are many 'recipes' for how much bicarbonate one should add. OBJECTIVE: To determine the acidity of lidocaine and the correct ratio of bicarbonate that should be added to neutralize lidocaine to achieve body pH. METHODS: Fifty samples each of commonly used anesthetics (lidocaine 1% and 2%, with and without epinephrine 1:100,000) were obtained and tested for pH. Data were also analyzed according to whether the vials had been previously opened. Ten additional samples of lidocaine 1% with 1:100,000 epinephrine were titrated against sodium bicarbonate 8.4% and tested for pH and the presence of precipitate. RESULTS: A solution of 1% lidocaine with 1:100,000 epinephrine had a mean (± SD) pH of 4.24±0.42, and 2% lidocaine with 1:100,000 epinephrine had a mean pH of 3.93±0.43. Plain 1% lidocaine had a pH of 6.09±0.16, and plain 2% lidocaine had a pH of 6.00±0.27. Epinephrine-containing solutions were more acidic when they had been previously opened. One per cent lidocaine with epinephrine required 8.4% sodium bicarbonate at a ratio of 1.1 mL:10 mL to 1.8 mL:10 mL to achieve the target tissue pH of 7.38 to 7.62. CONCLUSION: Lidocaine with epinephrine was approximately 1000 times more acidic than subcutaneous tissue. The addition of bicarbonate to the local anesthetic solution is simple to perform and is inexpensive. The proper volume ratio of 8.4% sodium bicarbonate to 1% lidocaine with 1:100,000 epinephrine is approximately 1 mL:10 mL. Surgeons should be more aware of the simplicity and value of buffering with bicarbonate to decrease the pain of injection.
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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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".