Çocukluk Çağı Akut Ensefalopati Vakalarında Beyin Omurilik Sıvısı Nöron Spesifik Enolaz Düzeyleri
Bibliographic record
Abstract
Objective: The aim of this study was to investigate the role of neuron-specific enolase (NSE) in cerebrospinal fluid (CSF) of pediatric acute encephalopathy patients and to investigate the role of NSE as a predictor of prognosis. Material and Methods: Twenty- six patients, between the ages of 4 months-13 years, who had been internalized in emergency department with the diagnosis of acute encephalopathy were enrolled in the study. Thirty patients who had undergone lumber puncture for the differential diagnosis of CNS infection without findings of infection constituted the control group. Patients were divided into three subgroups and 5 patients had been diagnosed as bacterial meningitis. Results: In 21 patients hospitalized in intensive care unit with the initial diagnosis of acute encephalopathy, and calculated GCS scores who underwent LP, mean CSF and serum NSE levels were 19.4±37.0 ug/L and 38.3±42.5 ug/L, respectively. In the control group, mean CSF NSE level was 2.6±1.1 ug/L, and mean serum NSE level was 10.1±3.4 ug/L. In cases with meningitis, mean CSF NSE level was 18.4±12.3 ug/L, and mean serum NSE level was 32±19.8 ug/L. The difference regarding CSF and serum NSE levels was significant between the acute encephalopathy group and control subjects (p<0.01). It was also significant between bacterial meningitis group and control group. The difference was only significant for CSF NSE levels when meningitis patients were compared with acute encephalopathy patients. There was significantly negative correlation between attending GCS and CSF NSE levels of acute encephalopathy patients (p<0.05). The relationship between CSF NSE levels and prognosis was significant in acute encephalopathy patients (p<0.05). Discussion and Conclusion: CSF NSE evaluation can be a useful marker for evaluation of neuronal damage and short term prognosis in children with acute encephalopathy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".