Ten Steps Toward Improving In-Hospital Cardiac Arrest Quality of Care and Outcomes
Bibliographic record
Abstract
mproving in-hospital cardiac arrest (IHCA) quality of care for adult and pediatric patients-not simply survival-requires a comprehensive set of programs and actions.Ideally, these should be embedded in a system of care that (1) plans and prepares for IHCA, (2) prevents IHCA when avoidable, (3) delivers high-quality, guidelinebased resuscitation, and (4) continuously evaluates and improves itself within a culture of person-centered care.IHCA is a high-risk event among hospitalized patients of all ages worldwide that is associated with significant morbidity and mortality.1 Estimates of its incidence vary across industrialized countries with rates in adults between 1.2 and 10 per 1000 hospital admissions, [2][3][4][5] which translates to ≈300 000 IHCA events in the United States each year with a reported survival rate to hospital discharge of ≈25%.5 Although experiences outside of higher income countries are limited, reported data suggest high incidence rates of IHCA in low-to middle-income countries like Uganda and China.6,7 These events are medical emergencies that require immediate treatment by teams of interdisciplinary health care professionals to optimize outcomes.Not surprisingly, there is a significant burden on hospitals to create and maintain resuscitation systems that are able to identify IHCA, activate an emergency response, and deliver high-quality resuscitation.Yet despite these pressures, there is consistent evidence that the quality of care around IHCA remains suboptimal and varies across hospitals and countries.[8][9][10] Given the variation in IHCA quality of care and outcomes, the International Liaison Committee on Resuscitation (ILCOR) launched an initiative to provide strategic guidance delineating critical steps to improve IHCA care.The member councils comprising ILCOR are the American Heart Association, European Resuscitation Council, Heart and Stroke Foundation of Canada, Australian and New Zealand Committee on Resuscitation, Resuscitation Councils of Asia, Indian Resuscitation Council Federation, and the collaborating organization, International Federation of Red Cross.The Ten Steps Toward Improving In-Hospital Cardiac Arrest Quality of Care and Outcomes in this document (Figure; Table) is the result of this effort and builds upon prior work in Key Words: adult ◼ cardiopulmonary resuscitation ◼ child ◼ humans ◼ incidence ◼ sudden cardiac arrest ◼ survival rate Downloaded from http://ahajournals.org
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".