Post‐operative hypercapnia‐induced hyperpnoea accelerates recovery from sevoflurane anaesthesia: a prospective randomised controlled trial
Bibliographic record
Abstract
BACKGROUND: The time to recovery from vapour anaesthesia is shortened by an increase in ventilation while maintaining normocapnia. Hypercapnia during emergence from anaesthesia in spontaneously breathing patients also increases anaesthetic clearance from the brain by increasing cerebral blood flow. We hypothesised that hypercapnia-induced hyperpnoea accelerates emergence from sevoflurane anaesthesia compared to the standard anaesthesia protocol. METHODS: After Ethics Review Board approval, 44 ASA I-III patients undergoing elective gynaecological surgery were randomised after surgery to either hypercapnic hyperpnoea or control groups. In the hypercapnic hyperpnoea group, the end-tidal CO2 was adjusted to a range of 6.0-7.3 kPa to maintain a minute ventilation of 10-15 l/min. Recovery indices were compared using unpaired t-tests and ANOVA. RESULTS: Prior to extubation, minute ventilation and end-tidal CO2 in hypercapnic hyperpnoea and control groups were 10.3 ± 1.7 l/min vs. 5.4 ± 1.2 l/min (P < 0.001) and 6.6 ± 0.6 kPa and 5.2 ± 0.5 kPa (P < 0.001), respectively. Compared to control, the study group had shorter time to extubation [4.4 ± 1.3 (SD) vs. 9.8 ± 4.4 min, P < 0.01], BIS recovery to > 75 (2.4 ± 0.9 vs. 6.1 ± 3.1 min, P < 0.01), eye opening (3.9 ± 1.6 vs. 9.8 ± 6.2 min, P < 0.01), eligibility for leaving operating room (5.1 ± 1.2 vs. 11.1 ± 4.6 min, P < 0.01), and post-anaesthesia care unit (73.9 ± 14.2 vs. 89.4 ± 22.6) CONCLUSION: Hypercapnic hyperpnoea in spontaneously breathing patients halves the time of recovery from sevoflurane-induced anaesthesia in the operating room.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".