Wellens’ Syndrome: An Atypical Localization After an Atypical Transient Left Bundle Branch Block Presentation
Bibliographic record
Abstract
Wellens’ syndrome is a clinical presentation with a particular pattern of electrocardiographic (ECG) T-wave changes associated with critical, proximal left anterior descending (LAD) coronary artery lesions. We report a case of a variant presentation of Wellens’ syndrome diagnosed after the resolution of a transient left bundle branch block (LBBB). The patient initially presented with acute coronary syndrome (ACS), which failed to be diagnosed despite LBBB presentation, but later became apparent after ECG converted to a Wellens’ type pattern. There are three major learning points from this case. First, Wellens’ syndrome can be difficult to recognize when presenting with an abnormal initial ECG-in our case it was only diagnosed after LBBB resolution. Second, Wellens’ syndrome can present with atypical localization of the offending lesion-our patient was ultimately identified to have a mid-LAD lesion. Third, there is no data in the literature regarding the time to T-wave recovery after revascularization of the LAD-our follow-up marks the timing at somewhere between 2 weeks and 4 months. J Med Cases. 2019;10(7): 193-197 doi: https://doi.org/10.14740/jmc3322
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".