Intravascular pulmonary metastases from sarcoma: appearance on computed tomography in 3 cases.
Bibliographic record
Abstract
INTRODUCTION: Various common malignant neoplasms (ie, liver, kidney, stomach, and breast) have been reported to embolize to the pulmonary arterial system. This uncommon occurrence can also result from metastatic sarcoma. We report 3 cases--2 chondrosarcomas and 1 osteosarcoma-associated with intravascular metastases to the pulmonary vasculature and discuss the clinical presentation and differentiating radiologic features on computed tomography (CT). DISCUSSION: Intravascular pulmonary tumour emboli may present with nonspecific respiratory symptoms or remain completely asymptomatic, and therefore, many patients are often misdiagnosed with thromboembolic disease or undiagnosed until autopsy. Chest CTs in all our patients demonstrated a striking pattern of multifocal tubular branching beaded opacities along the pulmonary vasculature in a multilobular distribution. CONCLUSION: Our observations and a review of the literature indicate that chest CT is the most useful diagnostic tool for detecting intravascular pulmonary tumour emboli. CT can distinguish this entity from mucous plugging by demonstrating the normal adjacent bronchus. The tubular nature of these metastases distinguishes them from the more common parenchymal metastases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".