PEALut in the Dietary Management of Patients with Acute Ischemic Stroke: A Prospective Randomized Controlled Clinical Trial
Bibliographic record
Abstract
Acute ischemic stroke (AIS), which represents 87% of all strokes, is caused by reduced blood supply to the brain associated with a prolonged inflammatory process that exacerbates brain damage. The composite containing co-ultramicronized Palmitoylethanolamide and luteolin (PEALut) is known to promote the resolution of neuroinflammation, being a promising nutritional approach to contrast inflammatory processes occurring in AIS. This study included 60 patients affected by acute ischemic stroke and undergoing thrombolysis. PEALut 770 mg was administered to 30 patients, twice daily for 90 days, in addition to the standard therapy. Neurological deficit, independence in activities of daily living, disability and cognitive impairment were investigated. In all patients, the severity of AIS defined by the NIHSS score evolved from moderate to minor (p < 0.0001). Patients’ independence in daily living activities and disability evaluated using BI and mRS showed a significant improvement over time, with a statistically significant difference in favor of PEALut-treated patients (p < 0.002 for BI, p < 0.0001 for mRS), who achieved also a marked improvement of cognitive function evaluated using MMSE and MoCA tests. PEALut proved to be a safe and effective treatment in addition to thrombolysis in the management of patients with 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".