Predisposing Factors, Pathologies, and Precipitating Factors Causing Intracerebral Hemorrhage
Bibliographic record
Abstract
Most people with spontaneous intracerebral hemorrhage (ICH) have hypertension, which is the strongest modifiable predisposing (risk) factor. However, multiple long-term medical conditions and other known predisposing factors for ICH usually coexist with hypertension, indicating that the causal pathway is multifactorial, and the term hypertensive ICH is oversimplistic. In this review, we integrate the highest quality evidence and our clinical experience in a framework to attribute multiple predisposing factors, underlying pathologies, and precipitating factors as the cause of ICH. In clinical practice, this framework supports physicians to take a holistic approach to treatment and prevention of ICH. In research, this framework shows how existing classification systems for the cause of ICH include underlying macrovascular, microvascular, and other structural pathologies but few predisposing or precipitating factors. Furthermore, this framework can inform the development of a more holistic classification system and expose knowledge gaps, including how predisposing factors lead to underlying pathologies and why only some people with these pathologies experience ICH.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".