MétaCan
Menu
Back to cohort
Record W2254475893 · doi:10.1259/bjr.20150954

Chest pain: coronary CT in the ER

2016· review· en· W2254475893 on OpenAlexaff
Erica Maffei, Sara Seitun, Andrea Igoren Guaricci, Filippo Cademartiri

Bibliographic record

VenueBritish Journal of Radiology · 2016
Typereview
Languageen
FieldMedicine
TopicCardiac Imaging and Diagnostics
Canadian institutionsUniversité de MontréalMontreal Heart Institute
Fundersnot available
KeywordsMedicineChest painCoronary artery diseaseRadiologyMyocardial perfusion imagingCardiac imagingAcute coronary syndromeModalitiesCardiologyIntensive care medicineMyocardial infarctionInternal medicine

Abstract

fetched live from OpenAlex

Cardiac CT has developed into a robust clinical tool during the past 15 years. Of the fields in which the potential of cardiac CT has raised more interest is chest pain in acute settings. In fact, the possibility to exclude with high reliability obstructive coronary artery disease (CAD) in patients at low-to-intermediate risk is of great interest both from the clinical standpoint and from the management standpoint. Several other modalities, with or without imaging, have been used during the past decades in the settings of new onset chest pain or in acute chest pain for both diagnostic and prognostic assessment of CAD. Each one has advantages and disadvantages. Most imaging modalities also focus on inducible ischaemia to guide referral to invasive coronary angiography. The advent of cardiac CT has introduced a new practice diagnostic paradigm, being the most accurate non-invasive method for identification and exclusion of CAD. Furthermore, the detection of subclinical CAD and plaque imaging offer the opportunity to improve risk stratification. Moreover, recent advances of the latest generation CT scanners allow combining both anatomical and functional imaging by stress myocardial perfusion. The role of cardiac CT in acute settings is already important and will become progressively more important in the coming years.

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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.843
Threshold uncertainty score0.476

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.029
GPT teacher head0.314
Teacher spread0.284 · 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 designOther design
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

Citations18
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueBritish Journal of RadiologySame topicCardiac Imaging and DiagnosticsFrench-language works237,207