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Record W3026920985 · doi:10.1007/s12928-020-00669-z

Contemporary use and trends in percutaneous coronary intervention in Japan: an outline of the J-PCI registry

2020· article· en· W3026920985 on OpenAlexaff
Mitsuaki Sawano, Kyohei Yamaji, Shun Kohsaka, Taku Inohara, Yohei Numasawa, Hirohiko Ando, Osamu Iida, Toshiro Shinke, Hideki Ishii, Tetsuya Amano

Bibliographic record

VenueCardiovascular Intervention and Therapeutics · 2020
Typearticle
Languageen
FieldMedicine
TopicCoronary Interventions and Diagnostics
Canadian institutionsVancouver General Hospital
FundersJapan Society for the Promotion of ScienceJapanese Association of Cardiovascular Intervention and Therapeutics
KeywordsConventional PCIPercutaneous coronary interventionMedicinePsychological interventionCertificationExcellenceInterventional cardiologyIntervention (counseling)Medical emergencyEmergency medicineIntensive care medicineCardiologyMyocardial infarctionNursing

Abstract

fetched live from OpenAlex

Cardiovascular interventions have achieved a level of excellence, with many outstanding advanced techniques and results. The mission of the Japanese Association of Cardiovascular Intervention and Therapeutics (CVIT) is to further our understanding of cardiovascular intervention and establish its procedural safety. [1] The Japanese Percutaneous Coronary Intervention (J-PCI) registry was established and sponsored by CVIT, and aims to provide basic statistics on the performance of percutaneous coronary interventions (PCI) in Japan. Today, the database has grown to become one of the largest healthcare procedural database with more than 200,000 cases registered annually from approximately 900 institutions in Japan representing over 90% of all PCI hospitals in the nation. Importantly, case registrations in the J-PCI registry are essential for coronary interventionalist and educating hospital certification. The present manuscript aimed to summarize the history of the J-PCI registry and outline the definitions of various items.

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 score0.383
Threshold uncertainty score0.576

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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)0.0000.000

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 head0.067
GPT teacher head0.298
Teacher spread0.232 · 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

Citations125
Published2020
Admission routes1
Has abstractyes

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