Massive pulmonary embolism with ST-elevation in the inferior leads and other interesting ECG findings
Bibliographic record
Abstract
Introduction: Pulmonary embolism is associated with many ECG findings, most of which are non-specific and most can be explained by the sudden severe increase in the right ventricular afterload leading to dysfunction, hypoperfusion, dilation and in rare very severe cases to ischemic injury. Many case reports described patients presenting with massive pulmonary embolism and very rare atypical ECG findings especially ST-segment elevation in the anteroseptal leads (V1-V4). Case presentation: We present a case of a 73-year old African American male who suffered from a massive pulmonary embolism with interesting ECG findings mainly ST-segment elevation in the inferior leads mimicking Inferior wall myocardial infarction. To our knowledge, this is the first case of ST-elevation in the inferior leads in the setting of a massive PE. Conclusion: The most likely explanation to the case is that the associated cardiac injury is multifactorial. Severe right ventricular dilation with significant increase in wall tension and oxygen consumption, sudden coronary hypoperfusion caused by the sudden drop in the right and left ventricular output, hypoxia caused by the massive PE and finally possible coronary spasm caused by hypoxia and increased right heart pressure might all have contributed to inducing the acute right ventricular ischemia which showed as ST-segment elevation in the inferior leads and an elevation in cardiac enzymes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".