The In-Hospital Code Stroke: A Look Back and the Road Ahead
Bibliographic record
Abstract
With increased patient volumes and complexity, stroke occurrence in hospitalized patients has become relatively more common. The process of activating a code stroke in-hospital differs in many institutions. An emergency team-based response to inpatient acute code stroke is warranted, with many protocols modeled similarly to the cardiac arrest response. However, several studies have demonstrated delays in recognition and management of acute stroke in-hospital as compared to those arriving directly to the emergency department (ED). Furthermore, there are several shared challenges with code stroke resuscitation in the ED and the ward, which include the assembly of ad hoc teams and requirement of access to urgent imaging. Delays in activating in-hospital code stroke contributes to increased morbidity, mortality, prolonged hospitalization, and associated health care costs. In the following commentary, we discuss the current landscape of acute in-hospital code stroke protocols, review the differences in neurologic outcomes between inpatient vs ED/out-of-hospital code stroke patients, and propose future directions for in-hospital code stroke paradigms for improved patient outcomes and quality of care.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".