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Record W4285806168 · doi:10.1016/j.jcin.2022.04.046

Pediatric and Congenital Interventional Cardiology Training

2022· review· en· W4285806168 on OpenAlexaff
Ryan Callahan, Eiméar McGovern, Stephen Nageotte, R Allen Ligon, Gareth J. Morgan, Troy A. Johnston, Audrey C. Marshall

Bibliographic record

VenueJACC: Cardiovascular Interventions · 2022
Typereview
Languageen
FieldMedicine
TopicCardiac, Anesthesia and Surgical Outcomes
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsMedicineInterventional cardiologyTraining (meteorology)CardiologyMedical physicsInternal medicineGeography

Abstract

fetched live from OpenAlex

ediatric interventional cardiology constitutes one of the fundamental disciplines within pediatric cardiac care, and cardiologists have now had the opportunity to train and practice in this dynamic field for over 40 years.Although difficult to determine precisely, the number of currently active pediatric interventional cardiologists in North America is estimated at 300.While early operators were largely self-taught, programs established training fellowships through the late 1980s and 1990s, and a core curriculum was first proposed in 1996. 1 This training guideline has been revised every decade based on society-sponsored expert consensus.2,3 In recent decades, fellows seeking advanced training in catheterization have competed for fewer than a dozen unaccredited fellowship positions in the United States and Canada.Absent an organized or centralized application process, the current system

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.012
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.001

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 head0.178
GPT teacher head0.370
Teacher spread0.193 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations4
Published2022
Admission routes1
Has abstractno

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