Code ICH: A Call to Action
Bibliographic record
Abstract
Intracerebral hemorrhage is the most serious type of stroke, leading to high rates of severe disability and mortality. Hematoma expansion is an independent predictor of poor functional outcome and is a compelling target for intervention. For decades, randomized trials aimed at decreasing hematoma expansion through single interventions have failed to meet their primary outcomes of statistically significant improvement in neurological outcomes. A wide range of evidence suggests that ultra-early bundled care, with multiple simultaneous interventions in the acute phase, offers the best hope of limiting hematoma expansion and improving functional recovery. Patients with intracerebral hemorrhage who fail to receive early aggressive care have worse outcomes, suggesting that an important treatment opportunity exists. This consensus statement puts forth a call to action to establish a protocol for Code ICH, similar to current strategies used for the management of acute ischemic stroke, through which early intervention, bundled care, and time-based metrics have substantially improved neurological outcomes. Based on current evidence, we advocate for the widespread adoption of an early bundle of care for patients with intracerebral hemorrhage focused on time-based metrics for blood pressure control and emergency reversal of anticoagulation, with the goal of optimizing the benefit of these already widely used interventions. We hope Code ICH will endure as a structural platform for continued innovation, standardization of best practices, and ongoing quality improvement for years to come.
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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 | 0.107 | 0.169 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.016 | 0.033 |
| Open science | 0.011 | 0.016 |
| Research integrity | 0.099 | 0.101 |
| Insufficient payload (model declined to judge) | 0.032 | 0.018 |
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".