Bibliographic record
Abstract
Clinical neurophysiology provides valuable information in neurosurgery, serving as: a diagnostic tool that can quantify type and severity of damage to the central and peripheral nervous system, a means of monitoring the safety of structures within and around the surgical site, and a method to map structures. As such it aides in identifying structures (e.g. finding sacral nerve roots within a spinal lipoma or nuclei within the thalamus), assessing functional integrity (e.g. motor pathways from cortex to any relevant accessible muscle), and monitoring their function while surgery occurs near to structures (e.g. VII while retraction during trigeminal microvascular decompression, and in scoliosis surgery) and provide guidance to technical operative steps (e.g. for selective dorsal rhizotomy). Intraoperative monitoring is not new, though the advances in equipment and technique of recent years have seen an explosion in the useful ways that neurophysiology can aid the neurosurgeon and protect the patient. The development of techniques to localize epileptic foci and map eloquent cerebral cortex in the 1950s produced major scientific advances as well as revolutionizing epilepsy surgery. Since the 1960s Tasker in Toronto, and Gillingham in Edinburgh, were recording from microelectrodes in the human thalamus to guide movement disorder surgery. Pioneers such as Møller have extended the applications of neurophysiological monitoring in skull base surgery. This chapter describes neurophysiological mapping and monitoring, and the different tools that are useful in different situations.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.058 | 0.046 |
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".