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Record W4392304007 · doi:10.1016/j.jacadv.2024.100863

Radiation Exposure, Training, and Safety in Cardiology

2024· article· en· W4392304007 on OpenAlexaff
Kamala P. Tamirisa, Mirvat Alasnag, Peter Calvert, Sabrina Islam, Anju Bhardwaj, Keerthana Pakanati, Shelley Zieroth, Mansour Razminia, Aarti Dalal, Mamas Mamas, Andrea M. Russo, Smadar Kort

Bibliographic record

VenueJACC Advances · 2024
Typearticle
Languageen
FieldMedicine
TopicRadiation Dose and Imaging
Canadian institutionsSt. Boniface Hospital
Fundersnot available
KeywordsMedicineSubspecialtyInterventional cardiologyRadiation exposureSpecialtyMedical physicsCardiologyCardiac imagingInternal medicineIntensive care medicineRadiologyPathologyNuclear medicine

Abstract

fetched live from OpenAlex

Exposure to ionizing radiation is an inherent occupational health hazard in clinical cardiology. Health risks have been reported previously, including predilection to cancer. In addition, orthopedic injury due to prolonged wearing of heavy protective lead aprons, which are mandatory to reduce radiation risk, have been extensively documented. Cardiology as a specialty has grown with rising volumes of increasingly complex procedures. This includes electrophysiological, coronary, and structural intervention, advanced heart failure/transplant management, and diagnostic imaging. Both the operator as well imaging specialists are exposed to radiation, particularly in structural interventions where interventional cardiologists and structural imagers work closely. Increasingly, women interested in cardiology may deselect the field due to radiation concerns. This expert document highlights the risks of radiation exposure in cardiology, including practical tips within various subspecialty fields such as interventional/structural cardiology, electrophysiology, imaging, advanced heart failure, and pediatric cardiology.

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)0.017
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 score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0120.003

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 head0.306
Teacher spread0.291 · 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

Citations32
Published2024
Admission routes1
Has abstractyes

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