Pathophysiology and Etiology of Nerve Injury Following Peripheral Nerve Blockade
Bibliographic record
Abstract
This review synthesizes anatomical, anesthetic, surgical, and patient factors that may contribute to neurologic complications associated with peripheral nerve blockade. Peripheral nerves have anatomical features unique to a given location that may influence risk of injury. Peripheral nerve blockade-related peripheral nerve injury (PNI) is most severe with intrafascicular injection. Surgery and its associated requirements such as positioning and tourniquet have specific risks. Patients with preexisting neuropathy may be at an increased risk of postoperative neurologic dysfunction. Distinguishing potential causes of PNI require clinical assessment and investigation; a definitive diagnosis, however, is not always possible. Fortunately, most postoperative neurologic dysfunction appears to resolve with time, and the incidence of serious long-term nerve injury directly attributable to peripheral nerve blockade is relatively uncommon. Nonetheless, despite the use of ultrasound guidance, the risk of block-related PNI remains unchanged. WHAT'S NEW: Since the 2008 Practice Advisory, new information has been published, furthering our understanding of the microanatomy of peripheral nerves, mechanisms of peripheral nerve injection injury, toxicity of local anesthetics, the etiology of and monitoring methods, and technologies that may decrease the risk of nerve block-related peripheral nerve injury.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".