Effect of PaCO2 and PaO2 on Lidocaine and Articaine Toxicity
Bibliographic record
Abstract
Alterations in arterial PaCO₂ can influence local anesthetic toxicity. The objective of this study was to evaluate the effect of stress-induced changes in PaCO₂ and PaO₂ on the seizure threshold of lidocaine and articaine. Lidocaine (2% with 1 : 100,000 epinephrine) or articaine (4% with 1 : 100,000 epinephrine) was administered intravenously under rest or stress conditions to 36 rats separated into 4 groups. Propranolol and prazosin were administered preoperatively to minimize cardiovascular effects of epinephrine. Mean arterial pressure (MAP), heart rate (HR), and arterial pH, PaCO₂, and PaO₂ were measured. Results showed no differences in MAP, HR, or pH. Stress significantly increased the latency period for the first tonic-clonic seizure induced by a toxic dose of both lidocaine and articaine (P < .05). Seizures were brought on more rapidly by articaine. No significant difference between toxic doses of lidocaine and articaine was noted. Stress raised the seizure threshold dose for both drugs and significantly (P < .01) increased arterial PaO₂ from 94.0 ± 1.90 mm Hg to 113.0 ± 2.20 mm Hg, and reduced PaCO₂ from 36.0 ± 0.77 mm Hg to 27.0 ± 0.98 mm Hg. In conclusion, reduction in PaCO₂ and/or increase in PaO₂ raised the seizure threshold of lidocaine and articaine. This study also confirmed that lidocaine and articaine have equipotent central nervous system toxicity.
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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 | 0.000 |
| 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) | 0.001 | 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".