APPLICATION OF THROMBOLYSIS THERAPY FOR ACUTE CEREBRAL CIRCULATION IN THE REPUBLIC OF KAZAKHSTAN, COUNTRIES OF THE FAR AND NEAR FOREIGN COUNTRIES.
Bibliographic record
Abstract
In the world, stroke suffers 5.6-6 million people a year. Stroke deaths are predicted to rise to 6.7 million in 2015 and to 7.8 million in 2030. Stroke is the leading cause of disability worldwide. The provision of specific therapy to patients with stroke in the form of thrombolytic therapy and neurosurgical operations are recognized international standards in the provision of medical care. The advent of computed tomography (CT) in the early 1980s made it possible to further study TLT. In 2014 Joanna M WardlawVeronic, MurrayEivind, Berge Gregory, J del Zoppo searched the Cochrane Stroke Trial Registry (Last November 2013), MEDLINE (1966 to November 2013) and at EMBASE (from 1980 to November 2013). We concluded that thrombolytic therapy administered within six hours of a stroke reduces the proportion of deaths or disability. The dependence of the efficacy and safety of fibrinolytic therapy on the time of its initiation has been demonstrated in a number of large studies. A pooled analysis of the results of the NINDS, ECASS I and II, ATLANTIS studies (n = 2775) showed that the odds ratio (OR) of a favorable outcome of the disease when thrombolysis was initiated in the first 90 minutes of a stroke. Thus, time is the most important condition for the effectiveness of TLT. In addition to the time factor, it is important to take into account the age of patients during TLT. F. Mateen et al. analyzed data from the Canadian TLT registry for patients aged 80 to 89 years and 90 to 99 years. Thus, thrombolysis in patients of various age groups, including those aged 80 to 89 years and older, is equally safe and effective.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".