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Worldwide Variation in the Use of Nuclear Cardiology Camera Technology, Reconstruction Software, and Imaging Protocols
Cole B. Hirschfeld, Mathew Mercuri, Thomas N.B. Pascual, Ganesan Karthikeyan, João V. Vítola, John J. Mahmarian, Nathan Better, S.E. Bouyoucef, Henry Hee-Seung Bom, Vikram Lele, Vincent Peter C. Magboo, Erick Alexánderson, Adel H. Allam, Mouaz H. Al‐Mallah, Sharmila Dorbala, Albert Flotats, Scott Jerome, Philipp A. Kaufmann, Osnat Luxenburg, Leslee J. Shaw, S. Richard Underwood, Madan M. Rehani, Diana Páez, Maurizio Dondi, Andrew J. Einstein, Raghava Kashyap, Joseph Meeks, Fernando Mut, Hee‐Seung Bom, S M Jerome, W. Amouri, H. Essabbah, S.S. Gassama, Khalid Makhdomi, G.I.E. El Mustapha, N. El Ouchdi, N. Qaïs, N. Soni, W. Vangu, R.M. Abazid, Brett Adams, Vikas Agarwal, Mahmoud Alfeeli, Naheel Alnafisi, Luis Borrego Bernabé, Gonca Bural, Tawatchai Chaiwatanarat, J.M. Chandraguptha, Gi Jeong Cheon, I. Cho, Ahmet Doğan, M. Eftekhari, Alex Frenkel, I. Garty, Susan R. George, Parham Geramifar, Haim Golan, S. Habib, Rafat Hussain, Hyung‐Jun Im, Hong Jun Jeon, Tek Chand Kalawat, Won Jun Kang, Felix Keng, Alisa Klaipetch, P.G. Kumar, Jaesung Lee, Won Woo Lee, Ilhan Lim, C.M.M. Macaisa, Gaurav Malhotra, BR Mittal, M.H. Mohammad, P. Mohan, Ivana Dewi Mulyanto, D. Nariman, U. Nayak, Khalid Niaz, G. Nikolov, Jerry Obaldo, Erkan Öztürk, Jong Min Park, S. Park, Chetan Patel, H.K. Phuong, Alvin Quinon, T.R. Rajini, Yuthana Saengsuda, Joel Ferreira Santiago, Haluk Sayman, Ajit Shinto, V. Sivasubramaniyan, Min Ho Son, Pushpalatha Sudhakar, Ghulam Syed, Nagara Tamaki, Kanungnij Thamnirat, Tanyaluck Thientunyakit, S. Thongmak, D.N. Velasco, Amit Verma, Usanee Vutrapongwatana, Yuliang Wang, Kyoung Sook Won, Zhenwei Yao, T. Yingsa-nga, Ryan Yudistiro, Nili Zafrir, S.C. Adrian, D. Agostini, S Aguadé, Gary C. Armitage, L. Magnus Bäcklund, Malin Backman, Mariwan Baker, Massimo Balducci, C. Bavelaar, Marko Berovic, Francesco Bertagna, R. Beuchel, A. Biggi, G Bisi, R. Bonini, A. 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Bibliographic record
VenueJACC. Cardiovascular imaging · 2021
Typearticle
Languageen
FieldMedicine
TopicCardiac Imaging and Diagnostics
Canadian institutionsMcMaster UniversityPublic Health OntarioUniversity of Toronto
FundersIrving Medical Center, Columbia UniversityNational Institutes of HealthGE HealthcareComissão Nacional de Energia NuclearInternational Atomic Energy AgencyEuropean Association of Nuclear MedicineMargaret Q. Landenberger Research FoundationWorld Health Organization
KeywordsMedicineCadmium zinc tellurideNuclear medicineMyocardial perfusion imagingMedical physicsGamma cameraRadiologyComputer sciencePerfusion
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)0.001
metaresearch head score (Gemma)0.002
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 score0.248
Threshold uncertainty score0.718
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 head0.019
GPT teacher head0.257
Teacher spread0.237 · 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.
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
Citations22
Published2021
Admission routes1
Has abstractno