Re: Comparison of Monitoring Techniques for Intraoperative Cerebral Ischemia
Bibliographic record
Abstract
monitoring that was not detected by SEPs.Disturbingly, the potential importance of this result is completely ignored in the discussion and conclusions.One other patient had a transient SEP change that resolved spontaneously without EEG alteration.However, the unconvincing illustration shows only a single average below the 50% criterion, which might be explained by the random effects of noise evident in the traces shown.Because carotid ischemia is usually widespread, both methods are very sensitive, but the situation may be different with more limited disturbances.Proper EEG provides the coverage required to demonstrate extensive or focal cortical ischemia.1 Although deep subcortical lesions may not cause EEG alterations, this appears to be very rare with good EEG technique.1,2 Somatosensory evoked potentials detect subcortical or cortical somatosensory pathway lesions, but are anatomically limited to this specific system only.Therefore, it is physiologically inevitable that motor and other non-sensory neurologic compromise without SEP change will occur exactly as has been found for other surgeries and documented during endarterectomy.3,4 It may have even occurred in one of the patients of this study, although apparently unappreciated by the authors.Depending on the undisclosed details of the critical case discussed above, the results of this study could support the contention that the suboptimal EEG techniques used were inadequate or that endarterectomy monitoring should involve both modalities.They do not demonstrate superiority of either, and should not persuade practitioners to rely primarily on SEP 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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".