Wake-Up Stroke: Incidence, Risk Factors and Outcome of Acute Stroke during Sleep in a Japanese Population. Takashima Stroke Registry 1988-2003
Bibliographic record
Abstract
Characterization of the time of stroke onset has been plagued by the problem of determining the time of the onset of events that are detected when the patient awakens. Our aim was to evaluate the characteristics, risk factors and acute fatality associated with wake-up stroke. Data was obtained from Takashima Stroke Registry covering approximately 55,000 residents in central Japan. During the period 1988-2003, information about the situation at stroke onset was available for 897 cerebral infarction (CI) and 335 intracerebral hemorrhage (ICH) events. Differences in characteristics and outcome between stroke during sleep and stroke while awake were explored. Among CI and ICH cases, 9.7 and 11.9% suffered from stroke during sleep, respectively. Hypertension and experiencing a severe event were associated with stroke during sleep among CI. Smoking and experiencing a severe event were associated with stroke during sleep and a drinking history reduced the chance of stroke during sleep among ICH. Acute fatality risks did not differ between stroke during sleep and stroke while awake among both CI and ICH cases. About 1 in 10 stroke patients had an onset of stroke during sleep. Hypertensive, smoker and clinically more severely affected patients had a higher prevalence of stroke during sleep. There were no differences between the 2 groups with respect to acute-case fatality.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".