American Society for Enhanced Recovery and Perioperative Quality Initiative Joint Consensus Statement on the Role of Neuromonitoring in Perioperative Outcomes: Cerebral Near-Infrared Spectroscopy
Bibliographic record
Abstract
Some neurological complications following surgery have been related to a mismatch in cerebral oxygen supply and demand that may either lead to more subtle changes of brain function or overt complications like stroke or coma. Discovery of a perioperative neurological complication may be outside the treatment window, thereby making prevention an important focus. Early commercial devices used differential spectroscopy to measure relative changes from baseline of 2 chromophores: oxy- and deoxyhemoglobin. It was the introduction of spatially resolved spectroscopy techniques that allowed near-infrared spectroscopy (NIRS)-based cerebral oximetry as we know it today. Modern cerebral oximeters measure the hemoglobin saturation of blood in a specific "optical field" containing arterial, capillary, and venous blood, not tissue oxygenation itself. Multiple cerebral oximeters are commercially available, all of which have technical differences that make them noninterchangeable. The mechanism and meaning of these measurements are likely not widely understood by many practicing physicians. Additionally, as with many clinically used monitors, there is a lack of high-quality evidence on which clinicians can base decisions in their effort to use cerebral oximetry to reduce neurocognitive complications after surgery. Therefore, the Sixth Perioperative Quality Initiative (POQI-6) consensus conference brought together an international team of multidisciplinary experts including anesthesiologists, surgeons, and critical care physicians to objectively survey the literature on cerebral oximetry and provide consensus, evidence-based recommendations for its use in accordance with the GRading of Recommendations, Assessment, Development and Evaluation (GRADE) criteria for evaluating biomedical literature. The group produced the following consensus recommendations: (1) interpreting perioperative cerebral oximetry measurements in the context of a preinduction baseline value; (2) interpreting perioperative cerebral oximetry measurements in the context of the physiologic variables that affect them; (3) using caution in comparing cerebral oximetry values between different manufacturers; (4) using preoperative cerebral oximetry to identify patients at increased risk of adverse outcomes after cardiac surgery; (5) using intraoperative cerebral oximetry indexed to preinduction baseline to identify patients at increased risk of adverse outcomes after cardiac surgery; (6) using cerebral oximetry to identify and guide management of acute cerebral malperfusion during cardiac surgery; (7) using an intraoperative cerebral oximetry-guided interventional algorithm to reduce intensive care unit (ICU) length of stay after cardiac surgery. Additionally, there was agreement that (8) there is insufficient evidence to recommend using intraoperative cerebral oximetry to reduce mortality or organ-specific morbidity after cardiac surgery; (9) there is insufficient evidence to recommend using intraoperative cerebral oximetry to improve outcomes after noncardiac surgery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".