An Evaluation of the Safety of Continuing Trastuzumab Despite Overt Left Ventricular Dysfunction
Bibliographic record
Abstract
Background: The major limitation in the use of trastuzumab therapy is cardiotoxicity. We evaluated the safety of a strategy of continuing trastuzumab in patients with breast cancer despite mild, asymptomatic left ventricular impairment. Methods: Charts of consecutive patients referred to a cardio-oncology clinic from January 2015 to March 2017 for decline in left ventricular ejection fraction (lvef), defined as a fall of 10 percentage points or more, or a value of less than 50% during trastuzumab therapy, were reviewed. The primary outcome of interest was change in lvef, measured before and during trastuzumab exposure and up to 3 times after initiation of cardiac medications during a median of 9 months. Results: All 18 patients referred for decline in lvef chose to remain on trastuzumab and were included. All patients were treated with angiotensin converting-enzyme inhibitors or beta-blockers, or both. After initiation of cardiac medications, lvef increased over time by 4.6 percentage points (95% confidence interval: 1.9 percentage points to 7.4 percentage points), approaching baseline values. Of the 18 patients, 17 (94%) were asymptomatic at all future visits. No deaths occurred in the group. Conclusions: Many patients with mildly reduced lvef and minimal heart failure symptoms might be able to continue trastuzumab without further decline in lvef, adverse cardiac events, or death when treated under the supervision of a cardiologist with close follow-up.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".