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Current Options for Mechanical Heart Technology

2008· review· en· W1989152604 on OpenAlexaff
David A. D’Alessandro, Vivek Rao, Paul L. DiGiorgi, Yoshifumi Naka, Mehmet C. Öz

Bibliographic record

VenueJournal of Cardiac Surgery · 2008
Typereview
Languageen
FieldEngineering
TopicMechanical Circulatory Support Devices
Canadian institutionsToronto General Hospital
Fundersnot available
KeywordsMedicineArtificial heartBridge to transplantationDestination therapyHeart transplantationHeart failureIntensive care medicineTransplantationBridge (graph theory)Ventricular assist deviceSurgeryCardiology

Abstract

fetched live from OpenAlex

Heart transplantation remains the treatment of choice for end-stage heart failure despite limited donor availability and allograft durability. Artificial heart technology was initially developed as a replacement for transplantation but the initial experience with these technologies was disappointing. The quest for a total artificial heart has largely been abandoned in favor of ventricular assist devices (VADs). VADs have gained widespread acceptance as bridge to transplant and bridge to recovery therapy. After more than a decade of clinical use, several FDA approved device designs have proved effective in treating patients with various causes of heart failure. This review describes the current, clinically available ventricular replacement and assist devices and defines the adult patient populations in which they are useful. The next generation of these devices will soon become available and their clinical utility will likely shape the future direction of heart failure therapy. Ultimately the concept of a long-term total artificial heart may be revisited.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.028
Threshold uncertainty score0.093

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0280.017

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.

Opus teacher head0.058
GPT teacher head0.319
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations6
Published2008
Admission routes1
Has abstractyes

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