Bibliographic record
Abstract
Tako‐Tsubo syndrome (TTS), or as previously known Tako‐Tsubo cardiomyopathy, is a form of transient LV systolic dysfunction that predominantly affects ageing women and is frequently but not always precipitated by an emotional or a physical stressor.1 A recent position statement on TTS in this journal provides a very comprehensive review of the clinical aspects of TTS along with the most recent ‘Heart Failure Association diagnostic criteria for TTS’.2 Mayo Clinic investigators proposed the initial definition of TTS in 2007.3 Since then, our understanding of TTS has significantly increased, and some important modifications have been made to the TTS diagnosis criteria by our colleagues from the Mayo Clinic, Sweden, Italy, and the European Society of Cardiology Heart Failure Association.2, 4-7 We consider TTS as a form of myocarditis where inflammation is induced by a sudden rise in catecholamine levels.1, 8 The evidence of inflammation of the myocardium in TTS has been confirmed by histological heart samples,9, 10 cardiac magnetic resonance imaging (MRI) studies,7 and by usual biochemical markers of inflammation.11 As per the recent position statement, viral myocarditis rightly remains an exclusion of TTS diagnosis. Similarly, other forms of infectious myocarditis should be excluded before making the diagnosis of TTS.
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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 | 0.002 | 0.012 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".