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Record W4205838134 · doi:10.3390/ijms23020723

Tissue Engineered Transcatheter Pulmonary Valved Stent Implantation: Current State and Future Prospect

2022· review· en· W4205838134 on OpenAlexaff
Xiling Zhang, Thomas Puehler, Jette Seiler, Stanislav N. Gorb, Janarthanan Sathananthan, Stephanie Sellers, Assad Haneya, Jan Hinnerk Hansen, Anselm Uebing, Oliver J. Müller, Derk Frank, Georg Lutter

Bibliographic record

VenueInternational Journal of Molecular Sciences · 2022
Typereview
Languageen
FieldMedicine
TopicTissue Engineering and Regenerative Medicine
Canadian institutionsSt. Paul's HospitalUniversity of British Columbia
FundersDeutsches Zentrum für Herz-Kreislaufforschung
KeywordsMedicineVentricular outflow tractValve replacementPulmonary valveCardiologyInternal medicineSurgeryIntensive care medicineStenosis

Abstract

fetched live from OpenAlex

Patients with the complex congenital heart disease (CHD) are usually associated with right ventricular outflow tract dysfunction and typically require multiple surgical interventions during their lives to relieve the right ventricular outflow tract abnormality. Transcatheter pulmonary valve replacement was used as a non-surgical, less invasive alternative treatment for right ventricular outflow tract dysfunction and has been rapidly developing over the past years. Despite the current favorable results of transcatheter pulmonary valve replacement, many patients eligible for pulmonary valve replacement are still not candidates for transcatheter pulmonary valve replacement. Therefore, one of the significant future challenges is to expand transcatheter pulmonary valve replacement to a broader patient population. This review describes the limitations and problems of existing techniques and focuses on decellularized tissue engineering for pulmonary valve stenting.

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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.032
GPT teacher head0.347
Teacher spread0.314 · 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

Citations10
Published2022
Admission routes1
Has abstractyes

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