Bibliographic record
Abstract
Extract For some anaesthetists neuroanaesthesia carries with it an air of mystique – difficult, long operations, equally difficult anaesthetics complicated by the need to avoid swings in blood pressure and coughing, but requiring a fully awake patient as soon as the procedure is finished. For others it is just another branch of surgical anaesthesia performed with an eye to the underlying surgical pathology, the application of relevant pharmacology and physiology and careful attention to detail. Good neuroanaesthetic practice encompasses both these views. The aim of this book is to provide trainees or those called upon to provide neuroanaesthesia on an occasional basis (such as colleagues working in non-neurosurgical centres who care for brain injured patients) with a contemporary guide to the underlying principles of neuroanaesthesia and their practical application. We are indebted to our colleagues whose contributions to this book describe a common-sense, scientifically-based approach to neuroanaesthesia and neuro critical care. Their clarity of thought and careful writing demonstrate that neuroanaesthesia can be practiced to a very high standard by the application of relatively few rules and guidelines. We are also indebted to our many other colleagues, particularly in the neurosurgical department in Nottingham, who have read, discussed, or, indeed, co-written many sections of this book. In particular we thank the following who, while not listed as authors, have made substantial contributions – Richard Ashpole, Graham Dow, Andy Norris, Iain Robertson and Barrie White.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.548 | 0.396 |
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".