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Record W2626113803 · doi:10.1097/rti.0000000000000275

Measuring Left Ventricular Size in Non–Electrocardiographic-gated Chest Computed Tomography

2017· review· en· W2626113803 on OpenAlexaff
Felipe Soares Torres, Luciano Folador, Diego André Eifer, Murilo Foppa, Kate Hanneman

Bibliographic record

VenueJournal of Thoracic Imaging · 2017
Typereview
Languageen
FieldMedicine
TopicCardiac Imaging and Diagnostics
Canadian institutionsToronto General HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineRadiologyComputed tomographyElectrocardiographyMultidetector computed tomographyTomographyCardiologyInternal medicine

Abstract

fetched live from OpenAlex

Non-electrocardiographic (ECG)-gated computed tomography (CT) of the chest is one of the most commonly performed imaging studies. Although the heart is included in every CT study, cardiac findings are commonly underreported in radiology reports. Left ventricular size is one of the most important prognostic markers in multiple cardiac diseases and can be measured on almost all non-ECG-gated multidetector chest CT studies. This review will discuss the available evidence on different measurements of left ventricular size obtained on non-ECG-gated CT of the chest. Measurement thresholds, technical issues, and potential problems are emphasized, with practical recommendations.

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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.042
GPT teacher head0.355
Teacher spread0.313 · 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

Citations5
Published2017
Admission routes1
Has abstractyes

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