Natural history of complications after intracerebral haemorrhage
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Numerous trials of haemostatic and neuroprotective agents for intracerebral haemorrhage (ICH) have failed. We characterized the risk of complications after ICH in a trial-eligible patient population, to inform safety in future trials. METHODS: We used the Virtual International Stroke Trials Archive database to identify placebo-treated patients with spontaneous ICH, who were not comatose at admission, where randomization took place within 4 h of symptom onset, and where serious complication and outcome data were available. We described the complications encountered and assessed whether the absence of common complications influenced attainment of good functional outcome (mRS < or =4) at 90 days using logistic regression. RESULTS: Of 201 patients examined, 70.2% experienced at least one serious complication. Neurological complications occurred in 21%, infections amongst 11%, and thromboembolic complications in 2%. Extension of the haemorrhage occurred most frequently: its absence was a significant predictor of good functional outcome (P < 0.0001, adjusted OR for good functional outcome = 21.9, 95% CI: [5.5, 88.3]). Neither infection, nor cardiac, nor thromboembolic complications influenced functional outcome at 90 days. CONCLUSIONS: Three month outcome in ICH patients depends on initial stroke severity and on enlargement of the haemorrhage. Our results should inform safety in future clinical trials of putative ICH therapies.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".