Patient with a total artificial heart maintained on outpatient dialysis while listed for combined organ transplant, a single center experience
Bibliographic record
Abstract
Advanced mechanical circulatory support is increasingly being used with more sophisticated devices that can deliver pulsatile rather than continuous flow. These devices are more portable as well, allowing patients to await cardiac transplantation in an outpatient setting. It is known that patients with renal failure are at increased risk for developing worsening acute kidney injury during implantation of a ventricular assist device (VAD) or more advanced modalities like a total artificial heart (TAH). Dealing with patients who have an implanted TAH who develop renal failure has been a challenge with the majority of such patients having to await a combined cardiac and renal transplant prior to transition to outpatient care. Protocols do exist for VAD implanted patients to be transitioned to outpatient dialysis care, but there are no reported cases of TAH patients with end stage renal disease (ESRD) being successfully transitioned to outpatient dialysis care. In this report, we identify a patient with a TAH and ESRD transitioned successfully to outpatient hemodialysis and maintained for more than 2 years, though he did not survive to transplant. It is hoped that this report will raise awareness of this possibility, and assist in the development of protocols for similar patients to be successfully transitioned to outpatient dialysis care.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".