Electrocardiographic approach to classification of acute pericarditis in emergency department: Typical and atypical pericarditis
Bibliographic record
Abstract
BACKGROUND: In typical pericarditis, concave ST–segment elevation can be characteristically seen in electrocardiogram (ECG). However, PR-segment depression may be the earliest ECG change in patients with acute pericarditis and in following time, from atypical pattern to typical pattern transition may be occur. Without ST-segment elevation in ECG may undergo misdiagnosed or overestimated condition, including acute coronary syndrome. Therefore, we classified acute pericarditis by highlighting ECG features to prevent any possible failure to notice acute pericarditis in emergency department (ED). METHODS: This study included 216 patients selected from the 2140 patients acute chest pain admitted into ED between 2015 and 2018. The two groups were retrospectively created by virtue of the presence or absence of typical ECG findings. Typical ECG refers to diffuse or regional concave ST-segment elevations with reciprocal ST-segment depression in aVR, and V1 in ECG, and atypical ECG refers to PR-segment depression in leads V5 to V6 in ECG. 100 patients (group I) had typical ECG, whereas 116 patients (group II) had atypical ECG changes. RESULTS: The mean age of the patients with typical pericarditis was higher than those with atypical pericarditis (P<0.05). Typical pericarditis group had higher CRP level (P<0.05). Atypical pericarditis group had more recurrence rate than typical pericarditis (P<0.05). In ECG following time, 10 patients with the atypical pericarditis pattern were transformed typical pericarditis pattern. CONCLUSION: We classified acute pericarditis as typical and atypical by highlighting ECG features to prevent any possible failure to notice acute pericarditis. Thanks to PR-segment recognition, acute pericarditis diagnosis may confirm and prevent the inappropriate coronary intervention. It is recommended that the ECG features should be examined thoroughly, especially with a focus on ST-segment elevation besides PR-segment depression.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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".