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Impact of the COVID-19 Pandemic on Global TAVR Activity
Xavier Armario, Jennifer Carron, Andrew J. Simpkin, Mohamed Elhadi, Ciara Kennedy, Mohamed Abdel‐Wahab, Sabine Bleiziffer, Thierry Lefèvre, Alexander Wolf, Thomas Pilgrim, Pedro Villablanca, Daniel J. Blackman, Nicolas M. Van Mieghem, Christian Hengstenberg, Martin J. Swaans, Bernard Prendergast, Tiffany Patterson, Marco Barbanti, John G. Webb, Miles Behan, Jon R. Resar, David Hildick‐Smith, Mark S. Spence, David Zweiker, Rodrigo Bagur, Rui Campante Teles, Flavio Ribichini, Dariusz Jagielak, Duk‐Woo Park, Ran Kornowski, Joanna J. Wykrzykowska, Matjaž Bunc, Rodrigo Estévez‐Loureiro, Karl Poon, Matthias Götberg, Raban Jeger, Hüseyin İnce, Erik Jerome Stene Packer, Marco Angelillis, Luis Nombela‐Franco, Yingqiang Guo, Mikko Savontaus, Abdulrahman M. Al-Moghairi, Catalina A. Parasca, Chad Kliger, David Roy, Levente Molnár, Mariana Silva, Jonathon White, Masanori Yamamoto, Pedro Carrilho‐Ferreira, Stefan Toggweiler, Vassileios Voudris, Yohei Ohno, Inês Rodrigues, Radosław Parma, Soledad Ojeda, Kostas Toutouzas, Ander Regueiro, Marek Grygier, Khaled Almerri, Ignacio Cruz‐González, Viliam Fridrich, José M. de la Torre Hernández, Stéphane Noble, Petr Kala, Lluís Asmarats, İbrahim Halil Kurt, Johan Bosmans, Mārtiņš Ērglis, Ivan P. Casserly, Dounia Iskandarani, Ravinay Bhindi, Joëlle Kefer, Wei‐Hsian Yin, Liesbeth Rosseel, Hyo‐Soo Kim, Stephen O’Connor, Farrel Hellig, Matías Sztejfman, Oscar Méndiz, Andrés M. Pineda, Ashok Seth, Elton Pllaha, Fábio Sândoli de Brito, Vilhelmas Bajoras, Mohammed Balghith, Michael Lee, Guering Eid‐Lidt, Bert Vandeloo, Vinicius D. Vaz, Mirvat Alasnag, Gian Paolo Ussia, Edgar Tay, Jorge Mayol, Sengottuvelu Gunasekaran, Gennaro Sardella, Wacin Buddhari, Hsien‐Li Kao, Antonio Dager, Apostolos Tzikas, Ingibjörg Jóna Guðmundsdóttir, Ahmad Edris, Luis Abel Gutiérrez Jaikel, Eduardo Arias, Mohammed Al‐Hijji, Mehmet Ertürk, César Conde‐Vela, Darko Boljević, Adolfo Ferrero Guadagnoli, Toomas Hermlin, Ahmed ElGuindy, Moysés de Oliveira Lima-Filho, Luciano de Moura Santos, Luís Pérez y Pérez, Gabriel Maluenda, Ali Rıza Akyüz, Imad A. Alhaddad, Haitham Amin, Chak-Yu So, Arif Al Nooryani, Carlos Vaca, Juan Albístur, Quang Ngoc Nguyen, Dabit Arzamendi, Eberhard Grube, Thomas Modine, Didier Tchétché, Kentaro Hayashida, Azeem Latib, Raj Makkar, Nicolò Piazza, Lars Søndergaard, John W. McEvoy, Darren Mylotte
Bibliographic record
VenueJACC: Cardiovascular Interventions · 2024
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsMcGill University Health CentreLondon Health Sciences CentreSt. Paul's Hospital
Fundersnot available
KeywordsPandemicMedicineSocioeconomic statusValve replacementIncidence (geometry)Public healthCoronavirus disease 2019 (COVID-19)Environmental healthDemographyInternal medicinePopulationDiseasePathology
Abstract
No abstract in any covered source. Its absence is recorded, not treated as a negative.
No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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 categoriesMeta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.112
Threshold uncertainty score0.974
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.036 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 head0.070
GPT teacher head0.447
Teacher spread0.377 · how far apart the two teachers sit on this one work
Validation status
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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
Citations11
Published2024
Admission routes1
Has abstractno