THE DIFFERENCE BETWEEN THERAPEUTIC WINDOW OF ISCHEMIC STROKEPATIENTS WITH HIGH EDUCATION LEVEL AND LOW EDUCATION LEVEL
Bibliographic record
Abstract
Background: Stroke is the third-leading cause of death in the United States, Canada, Europe, and Japan which most of these countries are developed, then how about developing county like Indonesia. According to the Global Burden of Disease Study 2010 (GBD 2010), cerebrovascular disease, tuberculosis, and road injury are the top three causes of years of life lost (YLLs) from 1990 to 2010 for Indonesia. Aim: To determine whether there is a difference between therapeutic window of stroke between patients with high education level and low education level. Method: This is a non-experimental research; it using the retrospective method by viewing the medical record files research subjects obtained from case report form of stroke registry. The research subjects are patients from RSUP Dr. Sardjito. Result: The correlation between the education level and onset of admission is weak because there were still more delayed patients than immediate patients in high educated group as well as low educated group. Since a p-value of 0.763 is higher than the accepted significant value of 0.05, null hypothesis is accepted. Conclusion: In summary, it is found that there is no strong relation between level of education in response to early onset or delay onset of hospital admission. This finding is supported by few studies that attempted to find relation on prior knowledge of stroke and immediate care. Patients with high education level and low education level are more likely to have delay therapeutic window which are mostly associated with poor health seeking behaviours.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".