Do we Need to Wake Patients up during Cortical Surgery?
Bibliographic record
Abstract
In recent years, a renewed fashion for awake surgery has appeared. In spite of its undoubted utility for scientific research, this technique has several limitations and flaws, usually not debated by parts of the scientific community. We will discuss the aims and limitations of cortical surgery, especially the points relevant to protecting the patient. These objectives should define the guidelines that direct clinical practice. We will review the awake technique as well as various tools used in intraoperative neurophysiological monitoring (IONM) to explore and monitor several cortical functions during long surgeries. The main topics discussed include electrocorticography (ECoG) and cortically recorded evoked potentials (EP), including somatosensory, visual and auditory. Later, we will discuss methods to identify and survey motor functions as motor-evoked potentials, although they are elicited trans-cranially. Finally, we will briefly discuss a promising technique to monitor some language functions in anaesthetized patients, such as cortico-cortical evoked potentials (CCEP). We will address in depth some technical questions about electrical stimulation whose full relevance are not always considered. Finally, we will discuss why, in the absence of empirical facts showing unequivocal superiority in post-surgical outcome, we have to awaken patients, especially when an alternate possibility exists without worst clinical results, as is the case for IONM.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".