Association between perioperative neuraxial local anesthetic neurotoxicity and arachnoiditis: a narrative review of published reports
Bibliographic record
Abstract
BACKGROUND/IMPORTANCE: Arachnoiditis is a rare but devastating disorder caused by various insults, one of which is purported to be local anesthetic neurotoxicity following neuraxial blockade. However, the relationship between local anesthetics administered into the neuraxis and the development of arachnoiditis has not been clearly elucidated. OBJECTIVE: We aimed to summarize the existing complex body of literature and characterize both the essential features and strength of any association between neuraxial local anesthetic neurotoxicity and arachnoiditis with a view toward mitigating risk, enhancing prevention, and refining informed consent discussions. EVIDENCE REVIEW: We reviewed all published reports of arachnoiditis attributed to local anesthetic neurotoxicity following perioperative neuraxial anesthesia. This narrative review was based on a systematic search methodology, which included articles published up until December 2022. FINDINGS: Thirty-eight articles were included, comprising 130 patients, over one-half of which were published prior to this century and inconsistent with modern practice. Neuraxial techniques included 78 epidurals, 48 spinals, and 5 combined spinal-epidurals, mostly for obstetrics. Reporting of essential procedural data was generally incomplete. Overall, at least 57% of patients experienced complicated needle/catheter insertion, including paresthesia, pain, or multiple attempts, irrespective of technique. The onset of neurological symptoms ranged from immediate to 8 years after neuraxial blockade, while the pathophysiology of arachnoiditis, if described, was heterogeneous. CONCLUSIONS: The existing literature attributing arachnoiditis to local anesthetic neurotoxicity is largely outdated, incomplete, and/or confounded by other potential causes, and thus insufficient to characterize the features and strength of any association.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".