Effects of dexmedetomidine on renal function, inflammatory markers, and cognitive functioning in elderly patients undergoing hip replacement surgery
Bibliographic record
Abstract
OBJECTIVE: To investigate the effects of dexmedetomidine on renal function, inflammatory markers, and cognitive outcome, and to identify factors influencing early postoperative cognitive dysfunction (POCD) in elderly patients undergoing hip replacement surgery. METHODS: A retrospective analysis was conducted on 162 elderly patients who underwent hip replacement surgery at Cangzhou Central Hospital from March 2022 to May 2023. Patients were divided into a control group (without dexmedetomidine) and an experimental group (with dexmedetomidine). Measurements included creatinine (Cr), blood urea nitrogen (BUN), interleukin 1β (IL-1β), tumor necrosis factor alpha (TNF-α), interleukin 6 (IL-6), Montreal Cognitive Assessment (MoCA) score, and the incidence of POCD seven days postoperatively. Univariate and logistic regression analyses were employed to investigate the predictors of early POCD. RESULTS: Significant differences were observed between the groups in terms of renal function, inflammatory markers, and cognitive outcome (Cr, BUN, IL-1β, TNF-α, IL-6 and MoCA scores) (all P<0.05). The experimental group showed a significantly lower incidence of POCD at seven days post-surgery (P<0.05). Logistic regression identified having a neuron-specific enolase (NSE) level seven days post-surgery ≥7.0 pg/ml as a risk factor for early POCD (P=0.001, OR=3.987, 95% CI: 1.789-8.886), whereas intraoperative use of dexmedetomidine was a protective factor (P=0.041, OR=0.424, 95% CI: 0.187-0.964). CONCLUSION: The use of dexmedetomidine in hip replacement surgery can mitigate postoperative renal injury and inflammatory response, enhance cognitive outcome, and significantly reduce the incidence and risk of early POCD in elderly patients.
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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 | 0.002 |
| 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".