Changes of serum neuron specific enolase and S100-β protein and their correlations with cognitive impairment in patients with moderate traumatic brain injury
Bibliographic record
Abstract
Objective To analyze the correlation of the levels of serum neuron specific enolase (NSE) and S100-β protein with cognitive impairment in patients with moderate traumatic brain injury (mTBI). Methods A retrospective case series study enrolled 87 patients with mTBI treated from January 2015 to October 2016. There were 50 males and 37 females, aged 14-60 years [(37.8±12.6)years]. The Glasgow Coma Score (GCS) was 9-12 points, among which were 9-10 points in 36 cases and 11-12 points in 51. The cognitive function was assessed by the Montreal Cognitive Assessment Scale (MoCA). The patients with MoCA<26 points were assigned into cognitive impairment group (study group, 54 cases), while the patients with MoCA ≥ 26 points was assigned into non-cognitive-impairment group (control group, 33 cases). The levels of serum NSE and S100-β protein were compared, and the correlation of levels of serum NSE and S100-β protein with cognitive dysfunction (assessed by MoCA and GCS) was analyzed. Results The levels of serum NSE and S100-βprotein were (35.7±11.0)ng/L and (1.8±0.5)ng/L, respectively in study group, which were significant higher than that in control group [(22.6±9.4)ng/L and (1.2±0.5)ng/L, respectively] (P<0.01). The levels of NSE [(33.7±10.0)ng/L] and S100-β [(1.7±0.4)ng/L] in patients with GCS 9-10 points were higher than those of NSE [(19.4±9.0)ng/L]and S100-β [(1.3±0.5)ng/L] in patients with GCS 11-12 points (P<0.01). The levels of serum NSE and S100-β protein in mTBI patients were negatively correlated with the MoCA score (r=-0.693, -0.721, P<0.05) and GCS (r=-0.527, -0.796, P<0.05). Conclusion The levels of serum NSE and S100-β protein are increased, and are correlated to the occurrence of cognitive impairment in patient with mTBI. Key words: Craniocerebral trauma; Cognition disorders; S100-β proteins; Neuron specific enolase
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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 | 0.000 |
| 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".