High Serum S100B Protein Levels As A Predictor Of Cognitive Function Disorders In Moderate Traumatic Brain Injury Patients
Bibliographic record
Abstract
Objective: To prove high serum S100B protein levels as a predictor of impaired cognitive function in patients with moderate traumatic brain injury. Method: A prospective cohort analytic observational study. The subjects are patients with a moderate degree of TBI with inclusion criteria: onset of TBI 24 hours, age 17-40 years old, and the exclusion criteria were the presence of impaired cognitive function pre-traumatic brain injury (Short IQCODE) 3), depression and multiple trauma. Patients/families/guardians who agreed to the informed consent were checked for S100B levels at <24 hours, then matched with the Montreal Cognitive Assessment version Indonesia (MoCA-INA) questionnaire on day 14 after TBI. This study’s data analysis consisted of univariate and bivariate analysis using Chi-Square. The significance level is stated with p <0.05, 95% confidence interval (CI) assisted by the IBM SPSS version 23. Results: There were 43 research subjects; 23 in the high S100B group and 20 in the S100B group were not high. The mean age is 28 years, with a ratio of 4:1 for men and females. Most of the years, education ≥12 years. The incidence of cognitive dysfunction in patients with moderate traumatic brain injury in the high serum S100B group was 76.19%, while in the non-high serum S100B group was 23.80%, with RR = 6.85, with 95% CI between 1.78-26 .36, and p-value = 0.005. Conclusion: High serum S100B protein levels as a predictor of cognitive function disorders were statistically significant in moderate traumatic brain injury patients with a risk of 6.85 times.
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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 | 0.000 | 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".