Is there any circadian variation consequence on acute case fatality of stroke? Takashima Stroke Registry, Japan (1990-2003)
Bibliographic record
Abstract
BACKGROUND: Circadian periodicity in the onset of stroke has been reported. However, it is unclear whether this variation affects the acute stroke case fatality. Time of the day variation in stroke case fatality was examined using population-based stroke registration data. METHODS: Stroke event data were acquired from the Takashima Stroke Registry, which covers a stable population of approximately 55,000 in Takashima County in central Japan. During the period of 1990-2003, there were 1080 (549 men and 531 women) cases with classifiable stroke onset time. Stroke incidence was categorized as occurring at night (midnight-6 a.m.), morning (6 a.m.-noon), afternoon (noon-6 p.m.), and evening (6 p.m.-midnight). The 28-day case fatality rates and 95% confidence intervals (95% CI) were calculated by gender, age, and stroke subtype across the time blocks. After adjusting for gender, age at onset, and stroke severity at onset, the hazard ratios for fatal strokes in evening, night, and morning were calculated, with afternoon serving as the reference. RESULTS: For all strokes, the 28-day case fatality rate was 23.3% (95% CI:19.4-27.6) for morning onset, 16.9% (95% CI:13.1-21.6) for afternoon onset, 18.3% (95% CI:13.6-24.1) for evening onset, and 21.0% (95% CI:15.0-28.5) for the night onset stroke. The case fatality for strokes during the morning was higher than the case fatality for strokes during afternoon. This fatality risk excess for morning strokes persisted even after adjusting for age, gender, and stroke severity on onset in multivariate analysis. CONCLUSION: In the examination of circadian variation of stroke case fatality, 28-day case fatality rate tended to be higher for the morning strokes.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".