Bibliographic record
Abstract
PURPOSE OF REVIEW: The quest for neuroprotection strategies during periods of neuronal vulnerability persists despite decades of basic and clinical research. This review will focus on the latest developments in the area of clinical brain protection with the major emphasis on strategies that can be beneficial during neurosurgical procedures. RECENT FINDINGS: Brain protection in neurosurgical patients may be achieved by nonpharmacological and pharmacological strategies. Pharmacological neuroprotection including anaesthetic administration have not been recently shown to be successful. Alternatively, nonpharmacological strategies including maintenance of cerebral perfusion by adequate control of mean arterial pressure (≥80 mmHg), liberal normoglycaemia (7.8-10 mmol/l), adequate haemoglobin levels (preoperative ≥120 g/l and intraoperative ≥90 g/l) and induction of hypertension (20-40% of preoperative values) in certain neurosurgical situations can be beneficial as neuroprotectants during neurosurgery. Mild hypothermia (32-35°C) failed to achieve neuroprotective effects in several situations of brain injury. SUMMARY: The findings of this review suggest that the anaesthesiologist is compelled to use nonpharmacological strategies sometimes based on empiric evidence to protect the brain during neurosurgical procedures. These strategies are simple, have high benefit/risk ratios and are inexpensive. Rigorous controlled clinical studies are needed to investigate the neuroprotective efficacy of these commonly used nonpharmacological methods.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".