Integrated ICH Care Pathway Across Stroke Service Levels: Time for a New Model of Care
Bibliographic record
Abstract
Intracerebral hemorrhage (ICH) is one of the leading causes of death and disability worldwide, and the limited number of proven treatments is a critical area of medical concern. Although tremendous advances have been made in our knowledge of the patterns, risk factors, prognosis, management, and prevention, there has been limited success in defining therapeutic strategies and care remains fragmented and haphazard. The establishment of targeted, timely, and comprehensive management for patients with ICH is an urgent and critical priority. This consensus statement proposes an integrated ICH care pathway across stroke service levels, embedding ICH-specific protocols, time targets, and structured follow-up within existing stroke systems to ensure timely, evidence-based, and comprehensive management. The integrated ICH care pathway is designed to optimize ICH management across multiple dimensions, facilitate rapid decision-making and the initiation of treatments that span emergency medical services, emergency departments, and inpatient units, and extend through discharge and follow-up. The primary objective is to reduce delays and reinforce seamless collaboration between services to ensure all patients receive optimal care. By integrating evidence-based protocols for acute management, secondary prevention, rehabilitation, and follow-up, the integrated ICH care pathway aims to improve outcomes from ICH and foster a standardized multidisciplinary care framework with the goal of alleviating the clinical burden and socioeconomic impact of 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 | 0.042 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.019 | 0.026 |
| Open science | 0.007 | 0.019 |
| Research integrity | 0.009 | 0.018 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".