Facial Swelling and Shortness of Breath Are Not Always an Allergic Reaction: It Could Be Acute Superior Vena Cava Syndrome
Bibliographic record
Abstract
Historically, it has been found that malignancy is associated with superior vena cava (SVC) syndrome. The past decade has seen more cases of thrombogenic and stenotic SVC syndrome due to increased use of pacemakers and indwelling central lines. As compared to the slowly progressing obstruction in malignancy, rapid thrombogenesis rate and a lack of venous collateral sequelae lead to more acute sequelae in these patients. It is important to timely assess patients presented with an acute process of SVC syndrome in the emergency room. Diagnosis can quickly be made by using computed tomography angiography (CTA) or magnetic resonance angiography (MRA) modalities. The underlying cause of the syndrome is the focus of the treatment. Anticoagulation is the basis of the treatment in the case of thrombogenic catheter-associated SVC syndrome. In order to promptly manage symptoms, it was observed that balloon angioplasty with stenting and thrombolytics proved to be beneficial. Herein we are describing a 68-year-old female with past medical history of colon cancer with liver metastasis on chemotherapy via port, presented to the emergency room with acute shortness of breath and facial and neck swelling, and was found to have acute superior vena cava syndrome.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".