P.118 An experience with combined cerebral oximetry and stump pressure measurement to guide shunt insertion during carotid endarterectomy
Bibliographic record
Abstract
Background: Cross-clamp ischemia during carotid endarterectomy (CEA) can cause perioperative stroke. Selectively shunting patients based on intraoperative monitoring modalities that assess risk for ischemia can reduce the occurrence of immediate stroke. An experience with combined cerebral oximetry and stump pressure measurement to direct selective shunting is presented here. Methods: Study comparing intraoperative monitoring data, the decision to shunt, and presence of immediate post-operative deficits. Patients were shunted if either cerebral oxygen saturation dropped by more than 10% by cerebral oximetry, or stump pressure during cross-clamping was less than 40 mmHg. Cross-clamp ischemia was determined by the presence of ipsilateral neurological deficit upon awakening. Results: 245 patients were included in this study. 22% were shunted. Patients who were not shunted were significantly more likely to have collateral blood flow detected on angiography. Immediate post-operative stroke was not encountered in any of the patients included in the study. One patient who met shunting criteria but was excluded since he could not have a shunt inserted due to difficult anatomy did suffer stroke. Conclusions: With the dual-monitoring criteria presented here, 22% of patients were shunted. With the exception of one patient who could not have a shunt placed, no immediate post-operative stroke was encountered.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".