Influence of Intracerebral Hemorrhage Location on Incidence, Characteristics, and Outcome
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The characteristics of intracerebral hemorrhage (ICH) may vary by ICH location because of differences in the distribution of underlying cerebral small vessel diseases. Therefore, we investigated the incidence, characteristics, and outcome of lobar and nonlobar ICH. METHODS: In a population-based, prospective inception cohort study of ICH, we used multiple overlapping sources of case ascertainment and follow-up to identify and validate ICH diagnoses in 2010 to 2011 in an adult population of 695 335. RESULTS: There were 128 participants with first-ever primary ICH. The overall incidence of lobar ICH was similar to nonlobar ICH (9.8 [95% confidence interval, 7.7-12.4] versus 8.6 [95% confidence interval, 6.7-11.1] per 100 000 adults/y). At baseline, adults with lobar ICH were more likely to have preceding dementia (21% versus 5%; P=0.01), lower Glasgow Coma Scale scores (median, 13 versus 14; P=0.03), larger ICHs (median, 38 versus 11 mL; P<0.001), subarachnoid extension (57% versus 5%; P<0.001), and subdural extension (15% versus 3%; P=0.02) than those with nonlobar ICH. One-year case fatality was lower after lobar ICH than after nonlobar ICH (adjusted odds ratio for death at 1 year: lobar versus nonlobar ICH 0.21; 95% confidence interval, 0.07-0.63; P=0.006, after adjustment for known predictors of outcome). There were 4 recurrent ICHs, which occurred exclusively in survivors of lobar ICH (annual risk of recurrent ICH after lobar ICH, 11.8%; 95% confidence interval, 4.6%-28.5% versus 0% after nonlobar ICH; log-rank P=0.04). CONCLUSIONS: The baseline characteristics and outcome of lobar ICH differ from other locations.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".