Brain BOLD MRI O <sub>2</sub> and CO <sub>2</sub> stress testing: Implications for perioperative neurocognitive disorder following surgery
Bibliographic record
Abstract
Abstract Respiratory end-tidal (ET) gas control is fundamental to anaesthetic management. The range of ET O 2 and CO 2 during the conduct of anaesthesia can significantly deviate from values in the awake state. Recent work shows ET CO 2 influences the incidence of perioperative neurocognitive disorder (POND). We examine the effects of controlled alterations in both ET O 2 and CO 2 on cerebral blood flow (CBF) in awake adults using BOLD MRI. Twelve healthy adults had BOLD and CBF responses measured to alterations in ET CO 2 and O 2 in various combinations commonly observed under anaesthesia. Dynamic alterations in regional BOLD and CBF were seen in all subjects with expected and inverse responses to both stimuli. These effects were incremental and rapid (within seconds). The most dramatic effects were seen with combined hyperoxia and hypocapnia. Inverse responses increased with age. Here we show that human brain CBF responds dramatically to alterations in ET respiratory gas tensions commonly seen during anaesthesia. Such alterations may impact the observed incidence of POND following surgery and intensive care, and is an important area for further investigation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".