Automated Data Abstraction of Cardiopulmonary Resuscitation Process Measures for Complete Episodes of Cardiac Arrest Resuscitation
Bibliographic record
Abstract
Abstract Background Cardiopulmonary resuscitation (CPR) process measures research and quality assurance has traditionally been limited to the first 5 minutes of resuscitation due to significant costs in time, resources, and personnel from manual data abstraction. CPR performance may change over time during prolonged resuscitations, which represents a significant knowledge gap. Moreover, currently available commercial software output of CPR process measures are difficult to analyze. Objective The objective was to develop and validate a software program to help automate the abstraction and transfer of CPR process measures data from electronic defibrillators for complete episodes of cardiac arrest resuscitation. Methods We developed a software program to facilitate and help automate CPR data abstraction and transfer from electronic defibrillators for entire resuscitation episodes. Using an intermediary Extensible Markup Language export file, the automated software transfers CPR process measures data (electrocardiogram [ECG] number, CPR start time, number of ventilations, number of chest compressions, compression rate per minute, compression depth per minute, compression fraction, and end‐tidal CO2 per minute). We performed an internal validation of the software program on 50 randomly selected cardiac arrest cases with resuscitation durations between 15 and 60 minutes. CPR process measures were manually abstracted and transferred independently by two trained data abstractors and by the automated software program, followed by manual interpretation of raw ECG tracings, treatment interventions, and patient events. Error rates and the time needed for data abstraction, transfer, and interpretation were measured for both manual and automated methods, compared to an additional independent reviewer. Results A total of 9,826 data points were each abstracted by the two abstractors and by the software program. Manual data abstraction resulted in a total of six errors (0.06%) compared to zero errors by the software program. The mean ± SD time measured per case for manual data abstraction was 20.3 ± 2.7 minutes compared to 5.3 ± 1.4 minutes using the software program (p = 0.003). Conclusions We developed and validated an automated software program that efficiently abstracts and transfers CPR process measures data from electronic defibrillators for complete cardiac arrest episodes. This software will enable future cardiac arrest studies and quality assurance programs to evaluate the impact of CPR process measures during prolonged resuscitations.
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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 | 0.007 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".