www.ipej.org 135 Interesting ECG Exercise-induced left septal fascicular block: an expression of severe myocardial ischemia
Bibliographic record
Abstract
The electrocardiogram (ECG) criteria for the left septal fascicular block (LSFB) are not universally accepted and many other denominations can be seen in literature: focal septal block, septal focal block, left septal fascicular block, left anterior septal block, septal fascicular conduction disorder of the left branch, left septal Purkinje network block, left septal subdivision block of the left bundle branch, anterior conduction delay, left median hemiblock, left medial subdivision block of the left bundle branch, middle fascicle block, block of the anteromedial division of the left bundle branch of His, and anteromedial divisional block. During exercise stress test, fascicular blocks (left anterior and posterior) seem to indicate severe coronary artery narrowing of left main coronary or proximal left anterior descending artery disease 1 and transient exercise-induced left septal fascicular block has been reported a few times 2,3. 54-year-old male, with a history of essential arterial systemic hypertension, primary hyperlipidemia and six-month typical chest pain during exercise (Class II – Canadian Cardiovascular Society) underwent an exercise stress test. During the exercise stress test, ECG demonstrated abrupt prominent anterior forces, an increase in R wave amplitude from V1 to V4, extreme left axis deviation and minor ST segment depression in DII, DIII and aVF (Figure 1).
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.011 |
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".