Local anaesthetic-induced seizures: a review
Bibliographic record
Abstract
Abstract: Local anaesthetics are indispensable drugs in both medical and dental practices, facilitating treatment through pain-free procedures by blocking nerve impulse transmission. Similar to any drug, despite their efficacy, local anaesthetics carry the risk of adverse events. There are many adverse events that can result from local anaesthetic administration. Among the most serious adverse events is the development of seizures, which has been well-documented in the literature. This review aims to explore the association between local anaesthetic administration and seizure development, along with the biological plausibility underlying this phenomenon. Epidemiological evidence highlights cases of seizures following local anaesthetic use, occurring across various medical and dental settings, demonstrating the relevance of this topic to current healthcare practice. To ensure patient safety, it is essential for healthcare practitioners to remain knowledgeable in both the prevention and management of this occurrence. The best form of management for any adverse event in healthcare is to prevent its occurrence in the first place. Prevention strategies should focus on minimizing risk factors that can result in seizures, including careful aspiration practices, and reducing the amount of local anaesthetic administered. The basic management of seizures entails prompt recognition and treatment, removal of dangerous objects, and monitoring of respiratory function.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".