Effect of dexmedetomidine on dynamic cerebral autoregulation and carbon dioxide reactivity during sevoflurane anesthesia in healthy patients
Bibliographic record
Abstract
BACKGROUND: There are conflicting opinions on the effect of dexmedetomidine on cerebral autoregulation. This study assessed its effect on dynamic cerebral autoregulation (dCA) using a transcranial Doppler (TCD). METHODS: Thirty American Society of Anesthesiologists physical status I and II patients between 18 and 60 years, who underwent lumbar spine surgery, received infusions of dexmedetomidine (Group D) or normal saline (Group C), followed by anesthesia with propofol and fentanyl, and maintenance with oxygen, nitrous oxide and sevoflurane. After five minutes of normocapnic ventilation and stable bispectral index value (BIS) of 40-50, the right middle cerebral artery flow velocity (MCAFV) was recorded with TCD. The transient hyperemic response (THR) test was performed by compressing the right common carotid artery for 5-7 seconds. The lungs were hyperventilated to test carbon dioxide (CO2) reactivity. Hemodynamic parameters, arterial CO2 tension, pulse oximetry (SpO2), MCAFV and BIS were measured before and after hyperventilation. Dexmedetomidine infusion was discontinued ten minutes before skin-closure. Time to recovery and extubation, modified Aldrete score, and emergence agitation were recorded. RESULTS: Demographic parameters, durations of surgery and anesthesia, THR ratio (Group D: 1.26 ± 0.11 vs. Group C: 1.23 ± 0.04; P = 0.357), relative CO2 reactivity (Group D: 1.19 ± 0.34 %/mmHg vs. Group C: 1.23 ± 0.25 %/mmHg; P = 0.547), blood pressure, SpO2, BIS, MCAFV, time to recovery, time to extubation and modified Aldrete scores were comparable. CONCLUSIONS: Dexmedetomidine administration does not impair dCA and CO2 reactivity in patients undergoing spine surgery under sevoflurane anesthesia.
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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) | 0.000 | 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 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".