A Pilot Study of a Novel Automated Somatosensory Evoked Potential (SSEP) Monitoring Device for Detection and Prevention of Intraoperative Peripheral Nerve Injury in Total Shoulder Arthroplasty Surgery
Bibliographic record
Abstract
INTRODUCTION: Peripheral nerve injury is a potentially devastating complication after total shoulder arthroplasty (TSA) surgery. This pilot study aimed to assess the feasibility of using an automated somatosensory evoked potential (SSEP) device to provide a timely alert/intervention to minimize intraoperative nerve insults during TSA surgery. METHODS: A prospective, single-arm, observational study was conducted in a single university hospital. The attending anesthesiologist monitored the study participants using the EPAD automated SSEP device and an intervention was made if there was an alert during TSA surgery. The median, radial, and ulnar nerve SSEP on the operative arm, as well as the median nerve SSEP of the nonoperative arm were monitored for each patient. All patients were evaluated for postoperative neurological deficits 6 weeks postoperatively. RESULTS: In total, 21 patients were consented and were successfully monitored. In total, 4 (19%) patients developed intraoperative abnormal SSEP signal changes in the operative arm, in which 3 were reversible and 1 was irreversible till the end of surgery. Median and radial nerves were mostly involved (3/4 patients). The mean cumulative duration of nerve insult (abnormal SSEP) was 21.7±26.2 minutes. Univariate analysis did not identify predictor of intraoperative nerve insults. No patients demonstrated postoperative peripheral neuropathy at 6 weeks. CONCLUSIONS: A high incidence (19%) of intraoperative nerve insult was observed in this study demonstrating the feasibility of using an automated SSEP device to provide a timely alert and enable an intervention in order to minimize peripheral nerve injury during TSA. Further randomized studies are warranted.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".