An update on mechanical circulatory support for heart failure therapy
Bibliographic record
Abstract
PURPOSE OF REVIEW: This article aims to review contemporary studies that utilized mechanical circulatory support (MCS) in the treatment of heart failure and to elaborate on prospective mechanical alternatives. RECENT FINDINGS: There is a growing need for a well-tolerated, durable and effective MCS option in patients with refractory heart failure. In previous years, the primary indication for MCS therapy supported bridge to transplantation. These early left ventricular assist devices (LVADs) suffered significant adverse events, thereby limiting their prolonged use. With the introduction of newer continuous flow LVADs, with lower morbidity, neurological events, pump failure and the expanded indication use (i.e. destination therapy), the overall number of implanted patients has grown. SUMMARY: There has been a dramatic advancement of durability found in the second and third-generation, continuous flow LVADs, along with improved survival rates in patients receiving these devices for destination therapy. MCS may soon become the treatment option of choice in refractory heart failure patients, especially with further evolution of less invasive approaches, smaller designs, and energy sources.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".