Life-Threatening Psoas Hematoma due to Retroperitoneal Hemorrhage in a COVID-19 Patient on Enoxaparin Treated With Arterial Embolization: A Case Report
Bibliographic record
Abstract
Respiratory failure is presumptively caused by microvascular thrombosis in some patients with coronavirus disease 2019 (COVID-19) requiring therapeutic anticoagulation. Anticoagulation treatment may cause life-threatening bleeding complications such as retroperitoneal hemorrhage. To the best of our knowledge, we report first case of a COVID-19 patient treated with therapeutic anticoagulation resulting in psoas hematoma due to lumbar artery bleeding. A 69-year-old patient presented with fever, malaise and progressive shortness of breath to our hospital. He was diagnosed with COVID-19 by RT-PCR. Due to an abnormal coagulation profile, the patient was started on enoxaparin. Over the course of hospitalization, the patient was found to have hypotension with worsening hemoglobin levels. Computed tomography scan of the abdomen and pelvis revealed a large psoas hematoma. Arteriogram revealed lumbar artery bleeding which was treated with embolization. Anticoagulation therapy, while indicated in COVID-19 patients, has its own challenges and guidelines describing dosages and indications in this disease are lacking. Rare bleeding complications such as psoas hematoma should be kept in mind in patients who become hemodynamically unstable, warranting prompt imaging for diagnosis and treatment with arterial embolization, thus eliminating need of surgical intervention.
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".