Cancer therapy related cardiac dysfunction as a result of Panitumumab
Bibliographic record
Abstract
Panitumumab is a human immunoglobulin monoclonal antibody designed to target the epidermal growth factor receptor (EGFR) which is used in the treatment of metastatic colorectal cancer alone or in combination with chemotherapy. In this report, we present a case of new onset heart failure with reduced ejection fraction in a patient following panitumumab therapy. A 73-year-old gentleman with metastatic rectal adenocarcinoma presented to his local hospital with increased shortness of breath, two months after his first and only dose of panitumumab. A transthoracic echocardiogram demonstrated dilated left ventricle with global hypokinesis and an estimated left ventricular ejection fraction of 25%. Our patient underwent a comprehensive diagnostic assessment at his presentation, including ECG, transthoracic echocardiogram, cardiac magnetic resonance, computed tomography coronary angiography (CTCA), invasive coronary angiogram and 18F-FDG PET-CT. These investigations revealed no evidence of ischemic events or inflammatory processes that could account for the severe left ventricular dysfunction. To our knowledge, this is the first reported case of heart failure with reduced ejection fraction linked to panitumumab with subsequent deep phenotyping. The current guidelines do not recommend specific cardiovascular monitoring protocols for patients receiving anti-EGFR monoclonal antibodies. Until more data are available, it would be prudent to implement the same cardiovascular surveillance measures outlined for individuals receiving osimertinib, which is an EGFR tyrosine kinase inhibitor.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".