Mechanical circulatory support: state of the art and future perspectives
Bibliographic record
Abstract
Mechanical circulatory support (MCS) has been viewed, until recently, as a rescue therapy to be applied when all else fails. Not surprisingly, this has resulted in suboptimal outcomes. Fortunately, the perseverance of a few dedicated groups has produced improved outcomes and the concept of MCS as an elective therapy is now steadily gaining acceptance. This is particularly true in the postcardiotomy setting, where a large number of new options are now available. The recently completed REMATCH study has demonstrated the feasibility and efficacy of permanent MCS as a therapy for end-stage heart failure, despite a high rate of device complications. Donor availability is decreasing and biological solutions will not be available for many years. New generation implantable rotary pumps, a fully implantable left ventricular assist device and a total artificial heart are all undergoing clinical evaluation, and several new exciting designs are in preclinical evaluation. A new paradigm for the treatment of terminal heart failure is emerging, where an unpredictable and expensive medically managed death in an intensive care unit setting is being exchanged for a more predictable high-cost, front-loaded therapy with management from the outpatient clinic. The perfusionist community has much to contribute to this emerging life support field, not only in the perioperative period, but also in providing ongoing technical support to hospital staff, recipients and their families.
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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 | 0.002 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".