Cerebral autoregulation derived blood pressure targets in elective neurosurgery
Bibliographic record
Abstract
Abstract Poor postoperative outcomes may be associated with cerebral ischaemia or hyperaemia, caused by episodes of arterial blood pressure (ABP) being outside the range of cerebral autoregulation (CA). Monitoring CA using COx (correlation between slow changes in mean ABP and regional cerebral O 2 saturation—rSO 2 ) could allow to individualise the management of ABP to preserve CA. We aimed to explore a continuous automated assessment of ABP OPT (ABP where CA is best preserved) and ABP at the lower limit of autoregulation (LLA) in elective neurosurgery patients. Retrospective analysis of prospectively collected data of 85 patients [median age 60 (IQR 51–68)] undergoing elective neurosurgery. ABP BASELINE was the mean of 3 pre-operative non-invasive measurements. ABP and rSO 2 waveforms were processed to estimate COx-derived ABP OPT and LLA trend-lines. We assessed: availability (number of patients where ABP OPT /LLA were available); time required to achieve first values; differences between ABP OPT /LLA and ABP. ABP OPT and LLA availability was 86 and 89%. Median (IQR) time to achieve the first value was 97 (80–155) and 93 (78–122) min for ABP OPT and LLA respectively. Median ABP OPT [75 (69–84)] was lower than ABP BASELINE [90 (84–95)] ( p < 0.001, Mann-U test). Patients spent 72 (56–86) % of recorded time with ABP above or below ABP OPT ± 5 mmHg. ABP OPT and ABP time trends and variability were not related to each other within patients. 37.6% of patients had at least 1 hypotensive insult (ABP < LLA) during the monitoring time. It seems possible to assess individualised automated ABP targets during elective neurosurgery.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".