Advances in Prehospital Management of Intracerebral Hemorrhage
Bibliographic record
Abstract
BACKGROUND: Spontaneous acute intracerebral hemorrhage (ICH) is associated with greater stroke-related disability and mortality than acute ischemic stroke. Hematoma expansion (HME), an important treatment target in acute ICH, is time-dependent, with a greater probability of hematoma growth occurring <3 h from ICH onset. SUMMARY: Promising treatment options to reduce HME include early intensive blood pressure reduction and the administration of hemostatic or anticoagulant reversal agents, yet large phase III clinical trials have so far failed to show overwhelming benefit for these interventions in acute ICH. Post hoc analyses provide evidence, however, that the therapeutic benefit of such treatments is enhanced by rapid and ultra-early intervention, likely driven in large part by attenuation of early HME. Clinical trials assessing ultra-rapid treatments (<2 h from ICH onset), including study procedures in the ambulance setting, are currently underway and demonstrate that the prehospital phase is a critical window for ICH management and an indispensable area of ICH research. Mobile stroke units, specialized ambulances equipped with imaging capabilities, can provide confirmatory diagnosis and expedite treatments. Nevertheless, multiple barriers (financial, organizational, geographical among others) hinder worldwide implementation. Emerging portable technologies as well as point-of-care measures of blood biomarkers show promise as feasible adjunct tools to discriminate ICH from acute ischemic stroke in the field and have the potential for widespread accessibility. KEY MESSAGES: Ultra-early interventions in acute ICH are likely necessary to mitigate the risk of HME, and as such, the prehospital setting is ideal to initiate time-sensitive ICH therapies. Reliable prehospital acute ICH detection is essential to provide disease-specific treatments. Overall, it is imperative that "Time is Brain" become the mantra not only for ischemic stroke but for ICH as well, and that the promise of ultra-early therapies for ICH be translated into concrete benefits for patients with this devastating condition.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".