Surgeon-driven neurophysiologic monitoring in a spinal surgery population
Bibliographic record
Abstract
BACKGROUND: This is a prospective observational study examining the use of a surgeon-driven intraoperative neurophysiologic monitoring system. Intraoperative neurophysiologic monitoring is becoming the standard of care for spinal surgeries with potential post-operative neurologic deficits. This standard applies to both adult and pediatric spinal surgery, but a shortage of appropriately trained and certified technologists and physiologists can compromise monitoring capabilities in some centers. A surgeon-driven, intra-operative monitoring system in the absence of a technologist or physiologist was examined for safety and efficacy. METHODS: One hundred thirty-five patients undergoing a variety of spinal procedures were monitored intra-operatively using a surgeon-driven neuro-monitoring system over a period of 80 months. Intraoperative monitoring included serial motor evoked potentials via an automated system that provided visual and audible feedback directly to the operative surgeon. Changes in monitoring and any corresponding surgical responses were evaluated and compared with postoperative neurological status. RESULTS: Of the 135 patients studied, intraoperative adjustments based on neuro-monitoring took place in four patients (3.0%): following reduction in spondylolisthesis, during instrumentation and fusion for a large kyphoscoliosis deformity, due to low hemoglobin, and because of traction. In all cases, surgical and/or anaesthetic modification restored MEPs toward baseline values. The accuracy of the neuro-monitoring results was sensitive to narcotics, benzodiazepines and changes in haemoglobin concentrations. No new postoperative deficits were observed in any patients in the cohort. CONCLUSIONS: The authors concluded that surgeon-driven neuro-monitoring was a safe and effective means of intraoperative neuro-monitoring during spinal surgery. It reliably detected intraoperative insults, which could potentially have resulted in postoperative neurologic compromise, and was not associated with any false-negative results in this cohort. Utility of surgeon-driven monitoring, using validated algorithms, may provide an option for this added safety measure even in cases where monitoring personnel are unavailable.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".