An Approach to Intraoperative Neurophysiologic Monitoring of Thoracoabdominal Aneurysm Surgery
Bibliographic record
Abstract
Thoracoabdominal aneurysm surgery carries an approximate 10% risk of intraoperative paraplegia. Abrupt cord ischemia and the confounding effects of systemic alterations and limb or cerebral ischemia challenges neurophysiologic spinal cord monitoring. This investigation sought a rapid differential monitoring approach to predict or help prevent paraplegia. Thirty-one patients were monitored with motor evoked potentials (MEPs) and median and tibial somatosensory evoked potentials (SSEPs). MEPs involved single-pulse transcranial electrical stimulation with D wave recording (n = 16), arm and leg muscle MEPs following multiple-pulse transcranial electrical stimulation (n = 12), or both (n = 3). D wave recordings required averaging, invasive epidural electrode insertion, and produced both false positives and false negatives. Muscle MEPs were instantaneous and reliably sensitive and specific for cord ischemia. Cortical and peripheral nerve SSEPs provided rapid detection of systemic alterations and cerebral or limb ischemia. Cord and subcortical SSEPs required excessive averaging time. In conclusion, bilateral arm and leg muscle MEPs with median and tibial peripheral nerve and cortical SSEPs provide sufficiently rapid detection and differentiation of cord ischemia from confounding factors. There were two predicted intraoperative spinal cord infarctions (6.5%) and nine circumstantial examples of possible contributions to deficit prevention.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".