Validation of a Clinical Decision Aid to Discontinue In-Hospital Cardiac Arrest Resuscitations
Bibliographic record
Abstract
CONTEXT: Most patients undergoing in-hospital cardiac resuscitation do not survive to hospital discharge. In a previous study, we developed a clinical decision aid for identifying all patients undergoing resuscitation who survived to hospital discharge. OBJECTIVE: To validate our previously derived clinical decision aid. DESIGN, SETTING, AND PARTICIPANTS: Data from a large registry of in-hospital resuscitations at a community teaching hospital in Georgia were analyzed to determine whether patients would be predicted to survive to hospital discharge (ie, whether their arrest was witnessed or their initial cardiac rhythm was either ventricular tachycardia or ventricular fibrillation or they regained a pulse during the first 10 minutes of chest compressions). Data from 2181 in-hospital cardiac resuscitation attempts in 1987-1996 involving 1884 pulseless patients were analyzed. MAIN OUTCOME MEASURE: Comparison of predictions based on the decision aid with whether patients were actually discharged alive from the hospital. RESULTS: For 327 resuscitations (15.0%), the patient survived to hospital discharge. For 324 of these resuscitations, the patients were predicted to survive to hospital discharge (sensitivity = 99.1%, 95% confidence interval, 97.1%-99.8%). In 269 resuscitations, patients did not satisfy the decision aid and were predicted to have no chance of being discharged from the hospital. Only 3 of these patients (1.1%) were discharged from the hospital (negative predictive value = 98.9%), none of whom were able to live independently following discharge from the hospital. CONCLUSION: This decision aid can be used to help physicians identify patients who are extremely unlikely to benefit from continued resuscitative efforts.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".