Bibliographic record
Abstract
Sir, Thank you for your comments regarding our article “Monitoring the depth of anesthesia using the new modified entropy sensors during supratentorial craniotomy: our experience”.[ 1 ] We completely agree the authors’ comments regarding that our study has major limitations and the study results should be interpreted with caution. We completely agree with the comment regarding the placement electrodes. Electrode #1 is indeed the reference electrode and should be placed in the between the eyebrows. We apologize for the error in the figure 2 in our manuscript. Electrode 1 should be in between the eyebrows and the electrode 2 should be at the temporal location. The aim of our study was to address the common problems with depth of anesthesia monitors in neurosurgical patients especially supratentorial craniotomies and to determine the feasibility of using the new GE entropy sensors in monitoring depth of anesthesia in these patients. Our study was a simple proof of concept observational study to show that it is feasible to monitor depth of anesthesia using a modified electrode placement with the new entropy sensors. These new sensors offer flexibility in terms of placement in modified positions. We did not do quantitative statistical analysis to show reliability and correlation with other depth of anesthesia indices (end-tidal anesthetic concentration or hemodynamic parameters) as this is not the primary objective of this study. Hence, we did not present the hemodynamic data in the manuscript, as this information did not provide any additional information to the manuscript. We did address some of these issues in the manuscript as the limitations of the study. There is a need for prospective study to determine the reliability of these sensors to monitor depth of anesthesia in the modified electrode placements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.020 | 0.030 |
| Insufficient payload (model declined to judge) | 0.019 | 0.013 |
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".