Personalized approach to assessing the functional result of acute ischemic stroke
Bibliographic record
Abstract
BACKGROUND: The main reason for the poor prognostic outcome in stroke patients is the polymorphism of cognitive and motor impairments. AIM: The purpose of this study is to evaluate the impact of clinical and paraclinical factors on the functional outcome of the patient in the acute period of ischemic stroke based on statistical methodology. MATERIALS AND METHODS: 160 patients of the primary vascular center with a diagnosis of “Ischemic stroke” were examined. Functional outcome parameters were designated as absolute values and were calculated as the difference between Montreal Cognitive Assessment (MoCA), National Institutes of Health Stroke Scale (NIHSS), Barthel Index (BI), Modified Rankin (mRS) scale scores before and after treatment. The following criteria were considered as factors influencing the prognosis of the functional result of acute stroke: demographic characteristics, parameters of cognitive function, stroke characteristics (localization, lateralization, subtype). Mathematical statistics was performed using the Python programming language and the Pandas and SciPy libraries. RESULTS: The mathematical model developed in this study made it possible to identify the main neuropsychological and clinical indicators that negatively and positively affect the functional result of acute stroke. As the main factors influencing the functional result in relation to MoCA, impairments in the areas of attention, speech and executive function, age, indicators on the IQCODE and ASPECTS scales were identified. Apraxia, agnosia, executive dysfunction, sex, age, lesion side, IQCODE parameters influenced the prognosis of the degree of patient’s daily activity according to IB. Regression of neurological symptoms according to NIHSS depended on indicators in the field of perception, praxis, speech, IQCODE and ASPECTS values. Semantic aphasia, mnestic and executive dysfunction, apraxia, and IQCODE scores were important for the prognosis of disability degree according to mRS. CONCLUSION: The use of discriminant analysis to predict the functional result will allow to create personalized diagnostic and therapeutic strategies for managing patients in the acute period of ischemic stroke. The predictive value of clinical and paraclinical markers in relation to the recovery of motor and cognitive function of patients may be useful in the further management of 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".