The significance of S100β protein on postoperative cognitive dysfunction in patients who underwent single valve replacement surgery under general anesthesia.
Bibliographic record
Abstract
OBJECTIVE: To analyze the effect of S100β protein on postoperative cognitive dysfunction (POCD) in patients who underwent single valve replacement surgery. PATIENTS AND METHODS: Mini Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) were applied to evaluate 178 patients who underwent single valve replacement surgery under general anesthesia from June 2014 to December 2015. Patients were assessed 1 day before surgery and on postoperative days 2 and 9. Thirty-two patients were identified as having postoperative cognitive dysfunction (the POCD group), while 146 cases did not experience POCD (the control group). A total of 155 healthy adult volunteers from the Medical Center were simultaneously chosen (healthy comparison group). Serum S100β levels from the three groups of patients were measured by ELISA. RESULTS: In the POCD group, serum S100β levels were significantly higher than those of the control group and healthy comparison group (p < 0.05). The postoperative length of stay in the hospital for patients in the POCD group was significantly increased (p < 0.05). CONCLUSIONS: The expression of serum S100β in patients with POCD was significantly increased. S100β may represent a potential target for the diagnosis and treatment of cognitive dysfunction after cardiac surgery under general anesthesia.
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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".