The role of neuron-specific enolase (NSE) and S100B protein in the incidence of acute postoperative cognitive dysfunction (POCD) in geriatric patients receiving general anesthesia
Bibliographic record
Abstract
Introduction: Postoperative cognitive dysfunction (POCD) following general anesthesia is frequent among geriatric patients worldwide. Neuroinflammation and neuronal injury have been associated with the incidence of POCD. Some biomarkers of brain damage including neuron-specific enolase (NSE) and S100B protein have been widely studied; however, their association with the incidence of POCD is still controversial. This study aimed to assess the correlation of serum NSE and S100B levels with the incidence of POCD among geriatric patients receiving general anesthesia. Methods: A prospective cohort study was conducted among geriatric patients receiving general anesthesia at Dr. Soetomo Hospital, Surabaya from July to October 2022. The Montreal Cognitive Assessment (MoCA) INA instrument was used to assess POCD, and enzyme-linked immunosorbent assay (ELISA) was used to quantify the levels of serum NSE and S100B. Spearman’s rank correlation was implemented to identify the correlation of MoCA INA scores with the levels of NSE and S100B. Mann-Whitney analysis was used to determine the association between NSE and S100B levels with the incidence of POCD. A p-value of ≤0.05 was considered statistically significant. Results: A total of 48 patients were enrolled in the study and 16.7% of them had POCD. Spearman’s correlation test suggested no significant correlation between MoCA INA score with serum NSE level (rs:-0.095; p=0.522) and S100B level (rs:-0.213; p=0.146). Mann-Whitney analysis indicated no significant difference in the NSE and S100B levels of patients with and without POCD (p=0.3470 and p=0.097, respectively). Conclusion: There was no significant association between NSE and S100B levels with the incidence of POCD among geriatric patients receiving general anesthesia during elective surgeries at Dr. Soetomo Hospital, Surabaya.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 teacher head, 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".