Validation of a Rule for Termination of Resuscitation in Out-of-Hospital Cardiac Arrest
Bibliographic record
Abstract
BACKGROUND: We prospectively evaluated a clinical prediction rule to be used by emergency medical technicians (EMTs) trained in the use of an automated external defibrillator for the termination of basic life support resuscitative efforts during out-of-hospital cardiac arrest. The rule recommends termination when there is no return of spontaneous circulation, no shocks are administered, and the arrest is not witnessed by emergency medical-services personnel. Otherwise, the rule recommends transportation to the hospital, in accordance with routine practice. METHODS: The study included 24 emergency medical systems in Ontario, Canada. All patients 18 years of age or older who had an arrest of presumed cardiac cause and who were treated by EMTs trained in the use of an automated external defibrillator were included. The patients were treated according to standard guidelines. Characteristics of diagnostic tests for the prediction rule were calculated. These characteristics include sensitivity, specificity, and positive and negative predictive values. RESULTS: Follow-up data were obtained for all 1240 patients. Of 776 patients with cardiac arrest for whom the rule recommended termination, 4 survived (0.5 percent). The rule had a specificity of 90.2 percent for recommending transport of survivors to the emergency department and had a positive predictive value for death of 99.5 percent when termination was recommended. Implementation of this rule would result in a decrease in the rate of transportation from 100 percent of patients to 37.4 percent. The addition of other criteria (a response interval greater than eight minutes or a cardiac arrest not witnessed by a bystander) would further improve both the specificity and positive predictive value of the rule but would result in the transportation of a larger proportion of patients. CONCLUSIONS: The use of a clinical prediction rule for the termination of resuscitation may help clinicians decide whether to terminate basic life support resuscitative efforts in patients having an out-of-hospital cardiac arrest.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".