Remote ischemic preconditioning improves the cognitive function of elderly patients following colon surgery
Bibliographic record
Abstract
BACKGROUND: Cognitive function impairment is one of the most common complications in elderly patients after surgery, and an ideal nonpharmacological therapy has not yet been identified. Thus, we hypothesized that remote ischemic preconditioning could improve cognitive functions in elderly patients after surgery and investigated the mechanism underlying this effect. METHODS: Ninety patients classified as American Society of Anaesthesiologists (ASA) physical status of 2 or 3 and aged 65 to 75 years who were scheduled for elective colon surgery under general anesthesia were randomly allocated to either a remote ischemic preconditioning group (Group R, n = 45) or a control group (Group C, n = 45). Remote ischemic preconditioning was performed by applying a static pressure of 200 mm Hg with a blood pressure cuff wrapped around the right upper limb for 3 ischemia cycles of 5 minutes each. RESULTS: The Montreal Cognitive Assessment (MoCA) scores between the 2 groups were not significantly different on the day before surgery or the seventh day after surgery, but the scores on the first day after surgery (26.87 ± 0.84 vs 25.96 ± 0.85, P < .001) and third day after surgery (27.49 ± 0.66 vs 27.02 ± 0.92, P = .009) were significantly higher for Group R than those for Group C. Moreover, remote ischemic preconditioning markedly decreased the serum concentrations of the interleukin-1β (IL-1β), tumor necrosis factor-α (TNF-α), and S100B proteins compared with the control group (P < .001). CONCLUSION: Remote ischemic preconditioning improves postoperative cognitive function in elderly patients following colon surgery. The cognitive protective effects of remote ischemic preconditioning are partially related to the inhibition of inflammation.
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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 | 0.000 |
| 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 | 0.000 |
| 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".