Comparative Effectiveness of Propofol-Sufentanil vs Propofol-Fentanyl in Elderly Patients Undergoing Transurethral Resection of the Prostate
Bibliographic record
Abstract
Objective: The aim of this study is to compare the anesthetic effect and cognitive function impact of propofol-sufentanil (PS) versus propofol-fentanyl (PF) in elderly patients undergoing transurethral resection of the prostate (TURP) under general anesthesia (GA). Methods: This is a retrospective cohort study conducted in First People's Hospital of Yongkang City. They included 98 patients aged 65 and above who underwent TURP under GA between October 2023 and March 2025. 49 patients who received PS were matched with a cohort of propofol-fentanyl (PF) in a 1:1 ratio. Compare the hemodynamic parameters (heart rate (HR) and mean arterial pressure (MAP)) of two groups at 30 minutes before anesthesia induction (T0), after anesthesia completion (T1), at the time of skin incision (T2), at the end of surgery (T3), at the postanesthesia care unit (PACU) (T4), 15 minutes after PACU arrival (T5), and 30 minutes after PACU arrival (T6). Compare two perioperative indicators (anesthesia onset time, postoperative awakening time, and extubation time). Compare the pain visual analogue scale (VAS) scores of two groups at three, 12, and 24 hours after surgery. And the Montreal Cognitive Assessment Scale (MoCA) was used to evaluate the number of patients with cognitive impairment at six, 24, and 72 hours after surgery in two groups. Compare the incidence of adverse reactions within 72 hours after surgery between two groups. Results: >0.05). Conclusion: PS is more effective for TURP anesthesia and has a better protective effect on early postoperative cognitive function.
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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) | 0.001 | 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".