O efeito de sugamadex sobre a função cognitiva e recuperação no pós‐operatório
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Sugammadex is the first selective relaxant binding agent. When compared with neostigmine, following sugammadex administration patients wake earlier and have shorter recovery times. In this study, we hypothesized that fast and clear awakening in patients undergoing general anesthesia has positive effects on cognitive functions in the early period after operation. METHODS: Approved by the local ethical committee, 128 patients were enrolled in this randomized, prospective, controlled, double-blind study. Patients were allocated to either Sugammadex group (Group S) or the Neostigmine group (Group N). The primary outcome of the study was early postoperative cognitive recovery as measured by the Montreal Cognitive Assessment (MoCA) and Mini Mental State Examination (MMSE). After baseline assessment 12-24h before the operation. After the operation, when the Modified Aldrete Recovery Score was ≥9 the MMSE and 1h later the MoCA tests were repeated. RESULTS: Although there was a reduction in MoCA and MMSE scores in both Group S and Group N between preoperative and postoperative scores, there was no statistically significant difference in the slopes (p>0.05). The time to reach TOF 0.9 was 2.19min in Group S and 6.47min in Group N (p<0.0001). Recovery time was 8.26min in Group S and 16.93min in Group N (p<0.0001). CONCLUSION: We showed that the surgical procedure and/or accompanying anesthetic procedure may cause a temporary or permanent regression in cognitive function in the early postoperative period. However, better cognitive performance could not be proved in the Sugammadex compared to the Neostigmine.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".