Bibliographic record
Abstract
Background: Recently, the prognosis of ischemic stroke, especially with major vessel occlusion cases, has been improving because of the spread of mechanical thrombectomy. However, there are some reports that the hospital-onset stroke has a poor prognosis as compared with the outpatient onset stroke. Materials and Methods: We studied six patients who developed cerebral infarction with main artery occlusion during hospitalization in the Department of Neurosurgery, Fukushima Redcross Hospital, between July 2014 and September 2016. Result: Four patients were male and two were female.Mean age was 75. 8±12. Reasons for admission were cardiac diseases in 3 patients (50%), rheumatoid arthritis in 1 patient, gastric cancer in 1 patient and cholecystitis in 1 patient. Median National Institute of Health Stroke Scale (NIHSS) score was 17, and median DWI-Alberta Stroke Programme Early CT Score (DWI ASPECTS) score was 8. Three patients received treatment with tPA, and five patients received mechanical thrombectomy. At discharge, 4 patients achieved mRS of 0 to 2, and 1 patient achieved mRS of 6. Mean time of Door to Picture time (D2P) was 20 min, Picture to Puncture time (P2P) was 79 min, Puncture to Reperfusion time (P2R) was 35 min. Conclusion: The prognosis of the cases studied this time was not necessarily bad, but the course of treatment should be improved. Further education on treatment for in-hospital onset stroke is necessary.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".