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Record W2976670365 · doi:10.1161/jaha.119.013160

Facilitated Data Relay and Effects on Treatment of Severe Aortic Stenosis in Europe

2019· article· en· W2976670365 on OpenAlexaff
Richard P. Steeds, Matthias Lutz, Jeetendra Thambyrajah, Antonio Serra, Eberhard Schulz, Jiří Malý, Marco Aiello, Tanja K. Rudolph, Guy Lloyd, Alessandro Santo Bortone, Karl Eugen Hauptmann, Alberto Clerici, Georg Delle‐Karth, Johannes Rieber, Massimo Mancone, Loïc Belle, Alexander Lauten, Martin Arnold, Berto J. Bouma, Cornelia Deutsch, Jana Kurucova, Martin Thoenes, Peter Bramlage, Norbert Frey, David Messika–Zeitoun

Bibliographic record

VenueJournal of the American Heart Association · 2019
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsUniversity of Ottawa
FundersEdwards Lifesciences
KeywordsStenosisCardiologyMedicineInternal medicineRelay

Abstract

fetched live from OpenAlex

Background Many patients with severe aortic stenosis are referred late with advanced symptoms or inappropriately denied intervention. The objective was to investigate whether a structured communication to referring physicians (facilitated data relay) might improve the rate and timeliness of intervention. Methods and Results A prospective registry of consecutive patients with severe aortic stenosis at 23 centers in 9 European countries with transcatheter as well as surgical aortic valve replacement being available was performed. The study included a 3‐month documentation of the status quo (phase A), a 6‐month intervention phase (implementing facilitated data relay), and a 3‐month documentation of a legacy effect (phase‐B). Two thousand one hundred seventy‐one patients with severe aortic stenoses were enrolled (phase A: 759; intervention: 905; phase‐B: 507). Mean age was 77.9±10.0 years, and 80% were symptomatic, including 52% with severe symptoms. During phase A, intervention was planned in 464/696 (67%), 138 (20%) were assigned to watchful waiting, 8 (1%) to balloon aortic valvuloplasty, 60 (9%) were listed as not for active treatment, and in 26 (4%), no decision was made. Three hundred sixty‐three of 464 (78%) patients received the planned intervention within 3 months. Timeliness of the intervention improved as shown by the higher number of aortic valve replacements performed within 3 months (59% versus 51%, P =0.002) and a significant decrease in the time to intervention (36±38 versus 30±33 days, P =0.002). Conclusions A simple, low‐cost, facilitated data relay improves timeliness of treatment for patients diagnosed with severe aortic stenosis, resulting in a shorter time to transcatheter aortic valve replacement. This effect was mainly driven by a significant improvement in timeliness of intervention in transcatheter aortic valve replacement but not surgical aortic valve replacement. Clinical Trial Registration URL : https://www.clinicaltrials.gov/ . Unique identifier: NCT 02241447.

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)0.004
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.015
GPT teacher head0.324
Teacher spread0.309 · 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 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

Citations13
Published2019
Admission routes1
Has abstractyes

Explore more

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