Automated Nerve Monitoring in Shoulder Arthroplasty: A Prospective Randomized Controlled Study
Bibliographic record
Abstract
BACKGROUND: Evoked potential monitoring is believed to prevent neurologic injury in various surgical settings; however, its clinical effect has not been scrutinized. It was hypothesized that an automated nerve monitor can minimize intraoperative nerve injury and thereby improve clinical outcomes in patients undergoing shoulder arthroplasty. METHODS: A prospective, blinded, parallel group, superiority design, single-center, randomized controlled study was conducted. Study participants were equally randomized into either the automated nerve-monitored or the blinded monitored groups. The primary outcome was intraoperative nerve injury burden as assessed by the cumulative duration of nerve alerts. Secondary outcomes were neurologic deficits and functional scores of the operative arm, and the quality of life index (Euro Quality of life-5 domain-5 level score) at postoperative weeks 2, 6, and 12. RESULTS: From September 2018 to July 2019, 213 patients were screened, of whom 200 were randomized. There was no statistically significant difference in the duration of nerve alerts between the automated nerve-monitored and control groups (median [25th, 75th interquartile range]: 1 [0, 18] and 5 [0, 26.5]; Hodges-Lehman difference [95% CI]: 0 [0 to 1] min; P = 0.526). There were no statistically significant differences in secondary outcomes between groups. However, in the ancillary analysis, there were reductions in neurologic deficits and improvements in quality of life index occurring in both groups over the course of the study period. CONCLUSIONS: Protection from nerve injury is a shared responsibility between surgeons and anesthesiologists. Although a progressive improvement of clinical outcomes were observed over the course of the study in both groups as a consequence of the real-time feedback provided by the automated nerve monitor, this trial did not demonstrate that automated nerve monitoring by itself changes important clinical outcomes compared with no monitoring.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".