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Record W3116684461 · doi:10.1097/hco.0000000000000825

Review of transapical off-pump mitral valve intervention with NeoChord implantation

2020· review· en· W3116684461 on OpenAlexaff
Ali Fatehi Hassanabad, William Kent, Corey Adams

Bibliographic record

VenueCurrent Opinion in Cardiology · 2020
Typereview
Languageen
FieldMedicine
TopicCardiac and Coronary Surgery Techniques
Canadian institutionsFoothills Medical CentreLibin Cardiovascular Institute of Alberta
Fundersnot available
KeywordsMedicineCardiologyInternal medicineMitral valve

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Mitral valve repair surgery has recently shifted from resection-based techniques to leaflet sparing approaches using synthetic neochordae. This has facilitated the growth of a new strategy of transapical off-pump mitral valve intervention with neochord implantation. RECENT FINDINGS: Minimally invasive approaches for mitral valve repair with robotic or video-assisted mini-right anterolateral thoracotomy have been developed to mitigate the morbidity associated with conventional median sternotomy. Recently, an alternative, less invasive surgical strategy has emerged. This transapical off-pump technique employs the NeoChord DS1000 (NeoChord, Inc., Minneapolis, MN, USA) system to achieve repair with neochordae via a left minithoracotomy incision. With appropriate patient selection, advanced cardiac imaging, and training in device deployment are important for procedural success. SUMMARY: Early results suggest that transapical off-pump mitral valve intervention with NeoChord implantation is a safe procedure with favorable outcomes for select patients with degenerative mitral regurgitation. Continued experience and clinical trials will assess the potential of this minimally invasive strategy, but this technique is likely to become part of the surgical repertoire for managing chronic degenerative mitral valve disease.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.080
GPT teacher head0.413
Teacher spread0.333 · 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 designSystematic review
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

Citations9
Published2020
Admission routes1
Has abstractyes

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