Role of serum ferritin as a prognostic marker in acute ischemic stroke: A preliminary observation
Bibliographic record
Abstract
Background: Acute ischemic stroke is an important cause of morbidity and mortality. Search has been on to find out the factors which can help in formulating the prognosis of acute ischemic stroke. One of the prognostic indicators, which has gained great clinical interest in recent times, is serum ferritin. Aims: To assess the serum ferritin levels in patients with acute ischemic stroke and to study the role of serum ferritin as a prognostic marker in these patients. Materials and Methods: This prospective, observational study was conducted on 50 patients of acute ischemic stroke aged ≥18 years who presented within 48 hours of onset of symptoms. Clinical severity of stroke was assessed at admission and on the 6th day using Canadian Stroke Scale (CSS), and serum ferritin levels were measured at admission and on the 6th day in all these subjects. Results: The mean serum ferritin levels at admission in patients with “more severe stroke” (CSS score at admission ≤7) and “less severe stroke” (CSS score at admission >7) were 282.77 ± 120.53 and 205.12 ± 110.96 ng/mL, respectively. The mean serum ferritin levels at admission were 173.71 ± 109.69 ng/mL in subjects who did not deteriorate and 336.86 ± 57.28 ng/mL in those who deteriorated, while the mean serum ferritin levels on the 6th day were 193.29 ± 101.88 and 343.95 ± 52.34 ng/mL in subjects who did not deteriorate and those who deteriorated, respectively. Conclusions: Serum ferritin has a significant positive correlation with the severity of acute ischemic stroke (P < 0.001), and the levels correlate with the outcome of the disease (P < 0.001); the patients with higher serum ferritin at admission tend to deteriorate more as compared to those with lower levels. Thus, serum ferritin can be used as a prognostic marker in acute ischemic 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".