MétaCan
Menu
Back to cohort
Record W1808591786 · doi:10.4103/2156-7514.163992

Identification of Cardiac and Aortic Injuries in Trauma with Multi-detector Computed Tomography

2015· review· en· W1808591786 on OpenAlexaff
Arvind Shergill, Tishan Maraj, Mark Barszczyk, Helen Cheung, Navneet Singh, Anna Zavodni

Bibliographic record

VenueJournal of Clinical Imaging Science · 2015
Typereview
Languageen
FieldMedicine
TopicTrauma Management and Diagnosis
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineComputed tomographyMultidetector computed tomographyRadiologyMajor traumaBlunt traumaTrauma centerBluntSurgeryRetrospective cohort study

Abstract

fetched live from OpenAlex

Blunt and penetrating cardiovascular (CV) injuries are associated with a high morbidity and mortality. Rapid detection of these injuries in trauma is critical for patient survival. The advent of multi-detector computed tomography (MDCT) has led to increased detection of CV injuries during rapid comprehensive scanning of stabilized major trauma patients. MDCT has the ability to acquire images with a higher temporal and spatial resolution, as well as the capability to create multiplanar reformats. This pictorial review illustrates several common and life-threatening traumatic CV injuries from a regional trauma center.

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)0.006
metaresearch head score (Gemma)0.001
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 score0.932
Threshold uncertainty score0.437

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
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 head0.116
GPT teacher head0.465
Teacher spread0.350 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Clinical Imaging ScienceSame topicTrauma Management and DiagnosisFrench-language works237,207