Functional prognosis of acute ischemic stroke patients from their neuropsychological features
Bibliographic record
Abstract
The objective was to evaluate the relationship between the patient neuropsychological condition and recovery of his/her motor activity disorder due to acute cerebrovascular accident. Materials and Methods. The study involved 103 patients with ischemic stroke in the pools of middle and anterior cerebral arteries. We carried out clinical and neurological assessments of the patient condition, and conducted neuropsychological testing via Beck Hopelessness Scale (BHS), Montreal Cognitive Assessment (MоCA), SF-36, and Visual Analog Scale (VAS). The examination was performed on three occasions: during the acute phase of cerebral circulatory disorders, after two weeks and within the range of 24-36 months. After the long-term follow-up, all patients were distributed among two groups: with a favorable outcome and with unfavorable outcome. We attempted to identify the most typical values of above-mentioned scales for each group. Results. The general physical health (GPhH) indicators based on the results of SF-36 (p=0.007), BHS (p=0.003 and 0.002), and VAS (p=0.025), collected after acute ischemic stroke, were prognostically significant. In both groups, the most important indicators for the stroke prognosis were MоCA (p=0.038), BHS (p=0.009) and SF-36 (GPH) (p=0.002), regardless of the stroke phase. Conclusion. The connection between the patient neuropsychological condition and restoring the motor functions after the stroke was revealed. The investigated questionnaires can be included in the multivariate forecast model of the stroke prognosis among other criteria for the outcome of this disease.
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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 | 0.001 | 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".