Perioperative Peripheral Nerve Injury After General Anesthesia: A Qualitative Systematic Review
Bibliographic record
Abstract
Perioperative peripheral nerve injury (PNI) is a well-recognized complication of general anesthesia that continues to result in patient disability and malpractice claims. However, the multifactorial etiology of PNI is often not appreciated in malpractice claims given that most PNI is alleged to be due to errors in patient positioning. New advances in monitoring may aid anesthesiologists in the early detection of PNI. This article reviews recent studies of perioperative PNI after general anesthesia and discusses the epidemiology and potential mechanisms of injury and preventive measures. We performed a systematic literature search, reviewed the available evidence, and identified areas for further investigation. We also reviewed perioperative PNI in the Anesthesia Closed Claims Project database for adverse events from 1990 to 2013. The incidence of perioperative PNI after general anesthesia varies considerably depending on the type of surgical procedure, the age and risk factors of the patient population, and whether the detection was made retrospectively or prospectively. Taken together, studies suggest that the incidence in a general population of surgical patients undergoing all types of procedures is <1%, with higher incidence in cardiac, neurosurgery, and some orthopedic procedures. PNI represent 12% of general anesthesia malpractice claims since 1990, with injuries to the brachial plexus and ulnar nerves representing two-thirds of PNI claims. The causes of perioperative PNI after general anesthesia are likely multifactorial, resulting in a "difficult to predict and prevent" phenomenon. Nearly half of the PNI closed claims did not have an obvious etiology, and most (91%) were associated with appropriate anesthetic care. Future studies should focus on the interaction between different mechanisms of insult, severity and duration of injury, and underlying neuronal reserves. Recent automated detection technology in neuromonitoring with somatosensory evoked potentials may increase the ability to identify at-risk patients and individualize patient management.
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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 | 0.009 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".