Role of Serum Ferritin and Prediction of Neurological Outcome in Acute Ischemic Stroke Patients
Bibliographic record
Abstract
Background: Stroke, also known as cerebrovascular accident (CVA), is an abrupt onset of neurological deficiency caused by a specific vascular aetiology. Aim: This study aimed to assess the role of serum ferritin in acute ischaemic stroke patients to correlate serum ferritin and neurological scales on the day of admission and during follow-up and to predict neurological outcomes. Methods: This prospective observational study included 100 patients who presented with acute ischaemic stroke clinically and with radiological imaging in the General Medicine and Neurology departments of Saveetha Medical College and Hospital for 18 months. Functional disability was assessed for all patients after 3 months based on the correlation of serum ferritin levels with the neurological scales, and the outcome was estimated. Results: The mean age of the patients was 59.45 ± 14.39. The serum ferritin level and Canadian Stroke Scale score during admission on day 7th and day 30th, were significantly different (p < 0.01). Serum ferritin levels were positively associated with the Glasgow Coma Scale score at admission and on the 7th and 30th days after admission. Ferritin levels were significantly higher in the moderate category under all circumstances (p < 0.01). Patients with mRS scores of 2, 3, and 4 were compared with serum ferritin levels and found to be significantly associated with serum ferritin levels. Conclusion: This study found that patients with higher serum ferritin levels experienced increased stroke severity and lower GCS and mRS scores, which correlated with increased morbidity.
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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 | 0.002 |
| 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 | 0.000 |
| 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".