The future of non-invasive cerebral oximetry in neurosurgical procedures: A systematic review
Bibliographic record
Abstract
Background:Cerebral hypoxia is one of the most important causes of secondary brain injury during neurosurgical procedures and in neurointensive care. In patients with brain injury, cerebrovascular reactivity may be impaired and a decrease in mean arterial pressure or cerebral perfusion pressure may lead to inadequate cerebral blood flow and secondary ischemia. There are several techniques available to measure brain oxygenation. In particular, near infrared spectroscopy (NIRS) is a non-invasive neuromonitoring technique and there has been a rapid expansion of clinical evidence that NIRS reduces perioperative neurologic complications. Methods:This systematic review synthesizes prospective and retrospective cohort studies that investigate the benefit of using NIRS in prevention of perioperative neurologic complications. The methodological quality of these studies is appraised. Results:Seven studies were included in this systematic review. The methodological quality of each study was assessed. They had representative patient populations, clear selection criteria and clear descriptions of study designs. Reproducible study protocols with ethics board approval were present. Clinical results were described in sufficient detail and were applicable to patient undergoing neurosurgical procedures and in neurointensive care. Limitations included small sample sizes and lack of reference standard. Conclusions:This systematic review synthesizes the most current evidence of non-invasive, inexpensive, and continuous measurement of cerebral oxygenation by NIRS. Results gained from these studies are clinically useful and shed light on how this neuromonitoring technique is beneficial in preventing perioperative neurological complications.
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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 | 0.020 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".