Local anesthetic systemic toxicity: current perspectives
Bibliographic record
Abstract
Local anesthetic systemic toxicity (LAST) is a life-threatening adverse event that may occur after the administration of local anesthetic drugs through a variety of routes. Increasing use of local anesthetic techniques in various healthcare settings makes contemporary understanding of LAST highly relevant. Recent data have demonstrated that the underlying mechanisms of LAST are multifactorial, with diverse cellular effects in the central nervous system and cardiovascular system. Although neurological presentation is most common, LAST often presents atypically, and one-fifth of the reported cases present with isolated cardiovascular disturbance. There are several risk factors that are associated with the drug used and the administration technique. LAST can be mitigated by targeting the modifiable risk factors, including the use of ultrasound for regional anesthetic techniques and restricting drug dosage. There have been significant developments in our understanding of LAST treatment. Key advances include early administration of lipid emulsion therapy, prompt seizure management, and careful selection of cardiovascular supportive pharmacotherapy. Cognizance of the mechanisms, risk factors, prevention, and therapy of LAST is vital to any practitioner using local anesthetic drugs in their clinical practice.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".