The Role of Citicoline in Neuroprotection and Neuro Repair in Acute Stroke
Bibliographic record
Abstract
Objective: To determine the efficacy and safety of Citicoline in acute stroke. Study Design: Comparative cross-sectional study. Place of Study and Duration: Combined Military Hospital, Jhelum Pakistan, from Dec 2017 to May 2018. Methodology: Thirty patients with a new onset of stroke, either ischemic or hemorrhagic, were included in the study. This sample of the population was further categorized into four Groups based on the National Institute of Health Stroke Scale scoring system. Half of the patients were medicated with Citicoline and standard stroke treatment and were examined as cases. The rest of the patients were treated with standard stroke management alone and were examined as Controls. The baseline guidelines of the patients were assessed by the Canadian Neurological Stroke Scale. However, for ease of comparison, the CNSS was converted into the National Institute of Health Stoke Scale using the following formula: NIHSS=23-2xCNSS. Results: In our study, baseline improvement in NIHSS score was higher in the Citicoline Group than in the Control Group(68% in the case Group vs. 53% in the Control Group). There was a 30% drop in NIHSS score in Cases compared to the ControlGroup (p>0.05). Conclusion: This study could not prove the effectiveness of Citicoline despite a favourable improvement in NIHSS score in cases. Though Citicoline is a well-tolerated and safe drug, it is ineffective in improving neurological outcomes in patients with acute stroke.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| 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".