Hemodialysis Catheter-Associated Right Atrial Thrombus Diagnosed via Point of Care Transesophageal Echocardiogram
Bibliographic record
Abstract
Catheter-associated right atrial thrombus (CRAT) is a potential complication of central venous catheter placement and is associated with an increase in morbidity and mortality. The precise incidence of CRAT is unknown, and there is a lack of clear screening and management guidelines for this condition. Additionally, the diagnosis is often missed when using transthoracic echocardiography (TTE) alone. Here, we present a case of a 64-year-old female admitted to the medical intensive care unit with multiorgan dysfunction who was diagnosed with hemodialysis catheter-associated right atrial thrombus (HDCRAT) via intensivist-performed point of care transesophageal echocardiography (TEE) after an initial TTE was negative. This patient was successfully treated with systemic anticoagulation, local thrombolysis, and delayed removal of the temporary hemodialysis catheter. Our experience serves to highlight the improved visualization of the right atrium and the diagnostic superiority of HDCRAT with TEE. We suspect that with greater utilization of TEE among intensivists, CRAT and HDCRAT will have increased recognition. It is imperative that intensivists are aware of this complication and various management strategies. Still, more studies are needed to establish clear management guidelines for CRAT and the associated complications.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".