The Impact on Patient Prognosis of Changes to the Method of Notifying Staff About Accepting Patients With Out-of-Hospital Cardiac Arrest
Bibliographic record
Abstract
Background: Our hospital is a designated emergency hospital and accepts many patients with out-of-hospital cardiac arrest (OHCA). Previously, after receiving a direct call from emergency services to request acceptance of an OHCA patient, the emergency room (ER) chief nurse notified medical staff. However, this method delayed ER preparations, so a Code Blue system (CB) was introduced in which the pending arrival of an OHCA patient was broadcast throughout the hospital. Methods: In this study, we retrospectively analyzed the impact of introducing CB at our hospital on OHCA patient prognosis to examine whether the introduction of CB is clinically meaningful. We compared consecutive cases treated before introduction of the CB (March 3, 2022, to March 22, 2023) with those treated afterwards (March 23, 2023, to July 23, 2024). Results: A total of 30 cases per group were included. The mean number of medical staff present at admissions increased significantly from 5.4 ± 0.6 to 15.0 ± 3.0 (P < 0.001). Although not statistically significant, the introduction of the CB increased the return of spontaneous circulation (ROSC) rate from 20% to 30%, survival to discharge rate from 3% to 10%, and social reintegration rate from 0% to 3%. ROSC occurred in 15 patients. Among OHCA patients with cardiac disease, the ROSC rate tended to increase from 0% to 43% (P = 0.055). In addition, in OHCA patients with cardiac disease whose electrocardiogram initially showed ventricular fibrillation or pulseless electrical activity, the ROSC rate increased from 0% to 100%. ROSC tended to be influenced by the total number of staff and physicians present and the number of staff such as medical clerks, clinical engineers, and radiology technicians (P = 0.095, 0.076, 0.088, respectively). Conclusions: Introduction of a CB may increase the ROSC rate and the number of patients surviving to discharge. It also appears to improve the quality of medical care by quickly gathering all necessary medical staff so that they can perform their predefined roles.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".