Progress in the Treatment of Acute Ischemic Stroke, Current Challenges and the Establishment of Clinical Decision-Making System
Bibliographic record
Abstract
Ischemic stroke often occurs in middle-aged and elderly people, leading to brain tissue ischemia, hypoxia and necrosis. The clinical manifestations are a series of neurological deficits, such as aphasia, hemiplegia and disturbance of consciousness, with high morbidity, mortality, disability rate, recurrence rate and multiple complications. This article aims to review current treatment advances, analyze current challenges and propose coping strategies. The literature on stroke treatment and the latest technological progress were reviewed. Combined with clinical and epidemiological to analyze the current challenges, the coping strategies were proposed before, during and after thrombolysis. Early intravenous thrombolysis and bridging treatment can restore blood perfusion in time and save the ischemic penumbra of brain tissue. However, the current proportion of patients receiving thrombolytic therapy is very low. The main challenges are as follows: easy to miss the time window, door-to-needle time is too long and there is a lack of understanding of the safety and efficacy of thrombolysis, especially the hemorrhagic transformation. A clinical decision-making system is established for stroke rescue by improving the popularization rate of stroke thrombolytic therapy, optimizing the green channel process of stroke and improving the executive ability of clinicians, to shorten the rescue time. Advanced imaging techniques are used to identify potential patients for thrombolysis. Acute intravascular bridge therapy is used to improve the efficacy of thrombolysis. Screening before thrombolysis, timely thrombolytic therapy, re-examination after thrombolysis and active response to hemorrhagic transformation can effectively improve the safety and acceptability of treatment. J Neurol Res. 2019;9(4-5):51-59 doi: https://doi.org/10.14740/jnr541
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".