Effects of predictive nursing intervention on cognitive impairment and neurological function in ischemic stroke patients
Bibliographic record
Abstract
BACKGROUND: Ischemic stroke is a clinical emergency caused by insufficient intracranial blood supply, which eventually leads to brain tissue necrosis and neurological impairment. Predictive nursing intervention has achieved impressive success in the nursing of multiple surgeries. However, the role of predictive nursing intervention in the care of patients with ischemic stroke remains unclear. METHODS: This study was a randomized controlled trial. Based on the inclusion and exclusion criteria, 126 patients were randomly assigned into two groups, namely the control group and the predictive nursing intervention group. Both groups were treated with thrombolytic therapy with alteplase. The patients in the control group were given routine nursing intervention and the predictive nursing intervention group received additional predictive care. Neurologic functions and cognitive impairment were evaluated by National Institutes of Health Stroke Scale (NIHSS), Fugl-Meyer assessment (FMA), Montreal cognitive assessment (MoCA), and mini-mental state examination (MMSE) scales, respectively. Door-to-Needle Times, venous thromboembolism (VTE)-related parameters, and complications were recorded. RESULTS: Predictive nursing intervention significantly shortened the Door-to-Needle Times and enhanced the peak/average femoral venous blood flow and femoral venous diameter. In addition, predictive nursing intervention improved the NIHSS, FMA, MMSE, and MoCA scores and remarkably reduced the recurrence of ischemic stroke, deep vein thrombosis and gingival bleeding. CONCLUSION: Predictive nursing intervention is beneficial to improve the effects of thrombolytic therapy in patients with ischemic stroke, which improves the neurological, cognitive and motor functions of patients, and reduces the occurrence of complications, suggesting an important clinical application value.
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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) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".