Dramatic Improvement of Pulmonary Tumor Thrombotic Microangiopathy in a Breast Cancer Patient Treated With Bevacizumab
Bibliographic record
Abstract
A 47-year-old woman diagnosed with stage IV left-sided breast cancer (T3N3aM1; OSS, HEP, LYM) 6 months back presented with respiratory distress. On admission, she developed respiratory failure, requiring 4 L of oxygen support. Pulmonary embolism was ruled out because computed tomography revealed no obvious pulmonary artery thrombus. Transthoracic echocardiography revealed a significant enlargement of the right ventricle and atrium. Pulmonary hypertension was confirmed via right heart catheterization. Pulmonary artery wedge aspiration cytology revealed adenocarcinoma cells. Based on these findings, we diagnosed the patient with pulmonary tumor thrombotic microangiopathy (PTTM) caused by breast cancer. Immediate chemotherapy (paclitaxel and bevacizumab) for breast cancer and concurrent treatment for pulmonary hypertension and disseminated intravascular coagulation were initiated. We could successfully control her condition with paclitaxel and bevacizumab for a year, and the patient survived for 1 year and 8 months. PTTM is a rare disease characterized by pulmonary hypertension and hypoxemia arising due to tumor embolization of the peripheral pulmonary arteries. PTTM is a rapidly progressing condition with no established treatment guidelines; its pathogenesis involves vascular endothelial growth factor (VEGF). This report highlighted the potential of bevacizumab, known for its anti-VEGF effect, in improving the pathological condition of patients with PTTM caused by breast cancer.
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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) | 0.001 | 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".