Diagnostic Value of Serum Neuron-Specific Enolase Level in Patients With Acute Ischemic Stroke; A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: We aim to assess the predictive value of serum neuron-specific enolase (NSE) level in patients with acute ischemic stroke referring to the emergency department. Methods: This systematic review and meta-analysis performed, considering the PRISMA and MOOSE statement guidelines. A computerized literature search of the known medical database conducted by using the relevant keywords. We included studies published before November 2016 in which stroke patients compared with non-stroke controls and also studies evaluating the serum levels of NSE in the study groups. Statistical analysis was pooled in a random effect model analysis using the Comprehensive Meta-Analysis software. Results: We included 12 articles in the qualitative and quantitative analysis, that their quality acceptable based on the Newcastle Ottawa Scale (NOS scale). The pooled effect estimates showed that NSE is significantly higher in ischemic stroke patients in comparison with their controls with a high effect estimate [OR 9.68, 95% CI (3.06 to 30.6)]. The effect estimate remained statistically significant under the fixed and random effects model. Conclusion: Our results show higher levels of NSE in patients with stroke than in the control group, indicating that NSE plays a role in the diagnosis of stroke. In terms of prognosis, there is evidence regarding the direct and indirect relationship; and it founded that serum levels of NSE is higher in larger stroke volume, which needs further research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.032 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".