Cardiac Surgical Nurses' Use of Atrial Electrograms to Improve Diagnosis of Arrhythmia
Bibliographic record
Abstract
BACKGROUND: The practice standard for electrocardiographic monitoring in hospitals recommends use of atrial electrograms after cardiac surgery to help diagnose cardiac arrhythmias. OBJECTIVES: To determine whether use of atrial electrograms significantly improves nurses' ability to diagnose cardiac arrhythmias and to assess nurses' perceptions of the ease of obtaining and interpreting electrograms, the frequency of use of atrial electrograms, and the correlation between nurses' experience with the technique and arrhythmia scores. METHODS: In total, 282 nurses completed a test consisting of 5 electrocardiographic rhythms for which use of atrial electrograms might improve interpretation. A standardized educational session on obtaining and interpreting atrial electrograms was given to 165 nurses who had not previously received such education. In a second test, the same rhythms were provided along with atrial electrograms to 261 nurses. Overall changes in total test scores and individual changes in interpreting rhythms were analyzed. Demographic information, perceptions of the ease of obtaining and interpreting atrial electrograms, and the frequency of use were collected. Correlation between scores on the second test and nurses' years of experience in interpreting atrial electrograms was determined. RESULTS: Use of atrial electrograms significantly increased overall arrhythmia interpretation scores. Nurses rated obtaining atrial electrograms as easy and interpreting the findings as moderately easy. Despite this reported ease, 57.1% of nurses obtained atrial electrograms less than monthly and only 3.4% obtained them daily. Correlation between experience with atrial electrograms and arrhythmia test scores was not significant. CONCLUSIONS: Nurses' use of atrial electrograms improves diagnoses of cardiac arrhythmias.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".