MétaCan
Menu
Back to cohort
Record W2508864159 · doi:10.1177/1460408616646587

Bedside identification of blunt thoracic aortic injury with point-of-care transesophageal echocardiography

2016· article· en· W2508864159 on OpenAlexaff
John H. Landau, Adam Power, W. Robert Leeper, Robert Arntfield

Bibliographic record

VenueTrauma · 2016
Typearticle
Languageen
FieldMedicine
TopicUltrasound in Clinical Applications
Canadian institutionsWestern University
Fundersnot available
KeywordsMedicineBluntThoracic traumaRadiologyFocused assessment with sonography for traumaCardiopulmonary resuscitationResuscitationPresentation (obstetrics)Thoracic aortaCardiologyAortaSurgery

Abstract

fetched live from OpenAlex

Trauma point-of-care ultrasound in the form of the Focused Assessment with Sonography for Trauma (FAST) exam and its evolution into extended FAST have significantly enhanced the diagnostic power of evaluation and resuscitation of the trauma patient; however, these modalities still have limitations in evaluating mediastinal and cardiac pathology. This report demonstrates a case of point-of-care transesophageal echocardiography in the diagnosis of blunt thoracic aortic injury in an unstable patient involved in a motor vehicle collision.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0030.003
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 head0.019
GPT teacher head0.335
Teacher spread0.317 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations10
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueTraumaSame topicUltrasound in Clinical ApplicationsFrench-language works237,207