Prediction of cerebral ischaemia during carotid endarterectomy with preoperative CO 2 -reactivity studies and angiography
Bibliographic record
Abstract
The objective of this study was to assess the value of combining the preoperative CO2 cerebrovascular reactivity index (CO2RI) with carotid and cerebral angiography in predicting the risk of severe cerebral ischaemia (SCI) during carotid endarterectomy (CEA). Seventy-four consecutive patients scheduled for CEA underwent preoperative digital subtraction angiography and CO2-reactivity tests. During CEA, cerebral function monitor (CFM) was used to document cortical electrical activity, whilst transcranial Doppler measured the middle cerebral artery flow velocity (FV). A persistent fall in CFM voltage and/or a fall in FV > or = 60% on internal carotid artery (ICA) clamping were used as criteria for defining SCI. Complete data from 59 patients were obtained for final analysis. Twelve cases showed a fall in FV > or = 60%; 11 of these also showed a sustained fall in CFM voltage. Using logistic regression, the risk of SCI was found to be negatively associated with (1) contralateral CO2RI, (2) the percentage stenosis of the contralateral ICA, and (3) the difference between ipsilateral and contralateral CO2RI. Using these factors, a logistic regression model for predicting the risk of SCI was established which provided a sensitivity of 75% and specificity of 100%. The risk of SCI during CEA was related to the contralateral ICA stenosis and the CO2RI of both cerebral hemispheres. This information may assist in presurgical planning and help to select asymptomatic carotid lesions for surgery.
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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 | 0.007 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".