Role of vitamin B12 deficiency in ischemic stroke risk and outcome
Bibliographic record
Abstract
Currently, ischemic stroke is the most prevalent form of stroke compared to hemorrhagic and there is a high incidence in older adults. Nutrition is a modifiable risk factor for stroke. B-vitamins are part of a metabolic network that integrates nutritional signals with biosynthesis, redox homeostasis, and epigenetics. These vitamins play an essential role in the regulation of cell proliferation, stress resistance, and embryo development. A deficiency in vitamin B12 is common in older adults and has been reported to be implicated in ischemic stroke. The aim of this review was to investigate whether vitamin B12 deficiencies impact the risk and outcome of ischemic stroke. Clinical data from our literature review strongly suggest that a deficiency in vitamin B12 is a risk factor for ischemic stroke and possible outcome. Our survey of the literature has identified that there is a gap in the understanding of the mechanisms through which a vitamin B12 deficiency leads to an increased risk of stroke and outcome. A vitamin B12 deficiency can increase homocysteine levels, which are a well-established risk factor for ischemic stroke. Another potential mechanism through which vitamin B12 deficient may impact neurological function and increase risk of stroke, is changes in myelination, however this link requires further investigation. Further studies are required in model systems to understand how a vitamin B12 deficiency changes the brain.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".