MétaCan
Menu
Back to cohort
Record W2318354818 · doi:10.4330/wjc.v5.i3.15

Can we still learn from single center experience after PARTNER?

2013· article· en· W2318354818 on OpenAlexaff
Benoit Daneault

Bibliographic record

VenueWorld Journal of Cardiology · 2013
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsCentre Hospitalier Universitaire de Sherbrooke
Fundersnot available
KeywordsMedicineValve replacementStenosisAortic valve replacementAortic valvuloplastyInternal medicineSingle CenterCardiologyAortic valve stenosisRandomized controlled trialSurgeryBalloonClinical trial

Abstract

fetched live from OpenAlex

With the publication of the Placement of Aortic Transcatheter Valves (PARTNER) trial, transcatheter aortic valve replacement (TAVR) has undoubtedly become the gold standard for severe aortic stenosis in patients that are not suitable candidate for surgical aortic valve replacement (AVR). The PARTNER trial also showed that TAVR is non-inferior to AVR in high-risk patients. A recent publication by Ben-Dor et al evaluated the outcome of high-risk patients with severe aortic stenosis who were referred to their institution for participation to the PARTNER trial. Only a minority of patients made it in the trial and the majority of patient ended being treated medically. Some patients were also treated with AVR outside the trial. The outcomes of all these patients were stratified by the treatment they received (AVR, TAVR or medical therapy with or without balloon aortic valvuloplasty). The 3 groups were different in their baseline characteristics. Ben-Dor et al found that patients treated medically had greater mortality than patients treated with TAVR or AVR. The survival of patients treated with TAVR was similar to those treated with AVR. Independent predictors of mortality were also found from their analysis. In this commentary, we discuss the finding of this study and compare it with the current literature.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.894

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.019
GPT teacher head0.308
Teacher spread0.289 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations0
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueWorld Journal of CardiologySame topicCardiac Valve Diseases and TreatmentsFrench-language works237,207