Heart Failure Association of the European Society of Cardiology Position Paper on the Management of Left Ventricular Assist Device-Supported Patients for the Non-Left Ventricular Assist Device Specialist Healthcare Provider: Part 2: At the Emergency Department
Bibliographic record
Abstract
The improvement in left ventricular assist device (LVAD) technology and scarcity of donor hearts have increased dramatically the population of the LVAD-supported patients and the probability of those patients to present to the emergency department with expected and non-expected device-related and patient-device interaction complications. The ageing of the LVAD-supported patients, mainly those supported with the 'destination therapy' indication, increases the risk for those patients to suffer from other co-morbidities common in the older population. In this second part of the trilogy on the management of LVAD-supported patients for the non-LVAD specialist healthcare provider, definitions and structured approach to the LVAD-supported patient presenting to the emergency department with bleeding, neurological event, pump thrombosis, chest pain, syncope, and other events are presented. The very challenging issue of declaring death in an LVAD-supported patient, as the circulation is artificially preserved by the device despite no other signs of life, is also discussed in detail.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.012 | 0.008 |
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".