A multidisciplinary approach for the emergency care of patients with left ventricular assist devices: A practical guide
Bibliographic record
Abstract
The use of a left ventricular assist device (LVAD) as a bridge-to-transplantation or destination therapy to support cardiac function in patients with end-stage heart failure (HF) is increasing in all developed countries. However, the expertise needed to implant and manage patients referred for LVAD treatment is limited to a few reference centers, which are often located far from the patient's home. Although patients undergoing LVAD implantation should be permanently referred to the LVAD center for the management and follow-up of the device also after implantation, they would refer to the local healthcare service for routine assistance and urgent health issues related to the device or generic devices. Therefore, every clinician, from a bigger to a smaller center, should be prepared to manage LVAD carriers and the possible risks associated with LVAD management. Particularly, emergency treatment of patients with LVAD differs slightly from conventional emergency protocols and requires specific knowledge and a multidisciplinary approach to avoid ineffective treatment or dangerous consequences. This review aims to provide a standard protocol for managing emergency and urgency in patients with LVAD, elucidating the role of each healthcare professional and emphasizing the importance of collaboration between the emergency department, in-hospital ward, and LVAD reference center, as well as algorithms designed to ensure timely, adequate, and effective treatment to patients with LVAD.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".