Delayed contrast enhancement cardiac magnetic resonance imaging in trastuzumab induced cardiomyopathy
Bibliographic record
Abstract
BACKGROUND: Trastuzumab (Herceptin), an antagonist to the human epidermal growth factor 2 (HER2) receptor significantly decreases the rates of breast cancer recurrence and mortality by 50%. Despite therapeutic benefits, the risk of cardiotoxicity with trastuzumab ranges from 10-15% when administered sequentially following anthraycline chemotherapy. Little is known about the utility of cardiac magnetic resonance (CMR) in the assessment of trastuzumab mediated cardiomyopathy. METHODS AND RESULTS: Between 2005-2006 inclusive, 160 breast cancer patients were identified at a single tertiary care oncology centre. Of the total population, 10 patients (mean age 40 +/- 8 years) were identified with trastuzumab induced cardiomyopathy, based on a LVEF less than 40% on serial MUGA or echocardiography. CMR was performed in all patients to determine LV volumes, systolic function and evidence of late gadolinium enhancement (LGE). At the time of diagnosis of trastuzumab induced cardiomyopathy, the mean LVEF was 29 +/- 4%. Subepicardial linear LGE was present in the lateral portion of the left ventricles in all 10 patients. CONCLUSION: LGE-CMR is a novel way of detecting early changes in the myocardium due to trastuzumab induced cardiotoxicity.
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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) | 0.000 | 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) | 0.001 | 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".