Outcome in Survivors of Middle Cerebral Artery Territory Ischemic Stroke: Can it be predicted?
Bibliographic record
Abstract
BACKGROUND: Stroke is the fourth leading cause of disability worldwide. The present study was designed to assess functional disability in middle cerebral artery (MCA) territory ischemic stroke patients by applying standard scales for stroke severity, cognitive impairment, disability, dependency and depression. We also wanted to study whether baseline assessment predicts outcome at 1 month. METHODOLOGY: After institutional ethics committee approval, patients were enrolled from the inpatients of the Department of Medicine at Topiwala National Medical College and BYL Nair Charitable Hospital, Mumbai from July 2014 to December 2015. Various clinical parameters were recorded on admission. On day 5(±1) the National Institutes of health Stroke Scale (NIHSS), Mini Mental state examination (MMSE) were administered. On 1 month follow up, these were repeated along with Modified Rankin scale, Barthel's index (BI) and Hospital Anxiety and Depression Scale (HADS). Presence of certain risk factors for stroke were reviewed at 1 month. RESULTS: 75 patients were enrolled. There was a delay in reaching the hospital and therefore imaging, in a greater majority. Only 4% could be imaged within the first 3 hours. Mean NIHSS score at day-5 was 9 and at day-30 was 6. Thus it had significantly reduced over 1 month. The MMSE remain unchanged at day 5 and at day 30. Lower baseline MMSE scores correlated with poorer outcomes on NIHSS, BI and mRS at 1 month. Both BI and mRS at 1 month indicated that about 60% of the cases had poor outcome. Amongst 48 of the non-aphasic MCA strokes, 11(22.92%) had depression. An NIHSS score of 6 or above on day 5, predicted poor outcome at 1 month. Presence of aphasia, dominant lobe affection and female sex were associated with a higher disability at 1 month. Around 30% cases had at least 1 risk factor uncontrolled at 1 month follow-up. CONCLUSIONS: Our findings show that disability assessment late in the first week after onset of stroke using NIHSS accurately forecast outcome at one month after onset of stroke. The MMSE too is not expected to change at 1 month. Those with aphasia are expected to have greater disability. Based on or study we recommend that stroke patients should be assessed with NIHSS and MMSE before discharge, to explain the prognosis of the patient. Also more intense counselling on controlling blood pressure and diabetes as well as abstinence from smoking should be undertaken routinely.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".