Three cases of venous thromboembolism in advanced cancer patients
Bibliographic record
Abstract
Background: The incidence of venous thromboembolism (VTE) in cancer patients is high and the symptoms of VTE observed in daily practice are varied. Here we report three cases of VTE treated at Department of Medical Oncology in Tohoku University. Case 1: A 42-year-old man diagnosed with urachal carcinoma had received FOLFIRI for 10 months. He felt his shortness of breath became strong for several weeks. Computed tomography (CT) revealed pulmonary embolism, thus he was hospitalized urgently. Heparin was administered for 1 week, and then direct oral anticoagulant (DOAC) was started. His shortness of breath was improved and CT showed the reduction of embolism, therefore he discharged 13 days after treatment started. CT after 3 months was confirmed the loss of thrombus and recurrence was not observed. Clinically relevant bleeding was not observed, therefore chemotherapy could be continued smoothly. Case 2: A 61-year-old man diagnosed with rectal cancer had received bevacizumab + FOLFIRI for 2 months. An asymptomatic thrombus around the catheter of central venous access port was accidently pointed out by CT to evaluate the effect of chemotherapy. DOAC was administered, and then CT examination after 1 months was confirmed the loss of thrombus. Recurrence was not observed and clinically relevant bleeding was not observed. Case 3: A 51-year-old man diagnosed with rectal cancer had received cetuximab + FOLFIRI for 2 months. His performance status was poor due to tumor progression and adverse events of chemotherapy. Pulmonary embolism was detected by CT to evaluate the effect of chemotherapy, thus he was hospitalized urgently and heparin and warfarin were administered. His prothrombin time-international normalized ratio did not remain within the target range, therefore DOAC was administered. However, the bleeding of his stoma was observed after 10 days, DOAC was discontinued. Anticoagulant therapy was never performed after that because of his poor systemic condition.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".