Monitoring the depth of anaesthesia using the new modified entropy sensors during supratentorial craniotomy: Our experience
Bibliographic record
Abstract
Abstract Background: Monitoring the depth of anaesthesia can be a challenge in patients undergoing supratentorial craniotomy because the conventional sensors for both bispectral index and entropy monitors lose their contact with a brain after scalp elevation. The new sensors for the entropy monitor are more flexible and can be placed in different locations. The purpose of this study was to determine the feasibility on the use of new GE entropy sensors in monitoring depth of anaesthesia in patients undergoing supratentorial craniotomy. Materials and Methods: We retrospectively reviewed the data from 20 consecutive patients undergoing supratentorial craniotomy who had the monitoring of the depth of anaesthesia using modified entropy sensors. Prior to the induction of anaesthesia, the new GE entropy sensor (P/N M1038681) was applied in a modified fashion. We measured the state entropy (SE) and response entropy (RE) at 12 perioperative time points. Entropy values were compared with the clinical indices of depth of anaesthesia. Results: Data from 20 consecutive patients (orbitozygomatic craniotomy [10] and bifrontal craniotomy [10]) were analysed. Monitoring was possible in all the patients. The changes in entropy values correlated with clinical indices of depth of anaesthesia. However, some patients showed variations in absolute values (RE and SE) during the intraoperative period without any changes in the level of anaesthetic depth. Conclusions: Monitoring the depth of anaesthesia is feasible with the use of new entropy sensors in patients undergoing supratentorial craniotomy. In contrast to standard sensors, the new sensors offer flexibility with the placement.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".