New ECG index as a marker of poor prognosis in acute pulmonary embolism: ST-segment elevation in lead aVR plus ST-segment depression in lateral leads
Bibliographic record
Abstract
Background: ST-segment elevation in lead aVR (STE-aVR) plus ST-segment depression in the lateral leads (STD-lat) is associated with a poor prognosis in acute coronary syndromes. The aim of the study was to determine the value of STE-aVR plus STD-lat in patients with acute pulmonary embolism (APE). Methods: We analyzed ECG and clinical data of 470 patients (pts) with APE, mean age 65.9±15.2 years old, female 274 pts. The new ECG index STE-aVR plus STD-lat was detected in 96 (20.8%) pts. ECG on admission was compared for STE-aVR plus STD-lat (+) (n= 96 pts) or (-) (n= 374 pts). Chi square and T student were used to compare dichotomic and continuous variables. A p value =/- than 0.05 was considered significant. Results: Comparison of both groups can be seen in the table. There were 50 (10.6%) cardiac deaths in the whole population. AF, atrial fibrillation; RBBB, right bundle branch block. Conclusions: The new ECG index STE-aVR plus STD-lat predicts poor prognosis in patients with APE. Patients with this ECG index present two-fold higher risk of complications, 2-fold higher risk of cardiac death, 3-fold more frequent use of inotropoic drugs and 4-fold higher need of ventilation support.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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 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".