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 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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".