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Record W2038392512 · doi:10.15273/dmj.vol34no1.4119

Imaging blunt traumatic aortic injury: a Canadian tertiary care trauma centre case series

2006· article· en· W2038392512 on OpenAlexaffvenueabout
Christopher B. Lightfoot, John M. Tallon, Robert J. Abraham

Bibliographic record

VenueDalhousie Medical Journal · 2006
Typearticle
Languageen
FieldMedicine
TopicAortic Disease and Treatment Approaches
Canadian institutionsDalhousie University
Fundersnot available
KeywordsBluntTertiary careBlunt traumaMedicineSeries (stratigraphy)Emergency medicineRadiologyGeology

Abstract

fetched live from OpenAlex

Few individuals survive blunt traumatic aortic injury (TAI); however, the mortality rate is dramatically lowered by timely diagnosis and treatment.We performed a retrospective review of patients that had undergone aortography or were entered in a trauma database with a diagnosis of TAI between December 1, 1996, and April 1, 2004.The diagnostic utility of computed tomography (CT) was compared to the gold standards of aortography and operative findings.Forty charts were reviewed.Fifty-two percent had indirect CT signs and 39 percent had direct CT signs of TAI.Six TAI's were surgically confirmed and repaired.Computed tomography for TAI had a sensitivity of 100 percent and a specificity of 74 percent.Computed tomography imaging has obviated aortography in screening for TAI.Aortography continues to have a valuable diagnostic role prior to surgical management.

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)0.000
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: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.422
Threshold uncertainty score0.849

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.005
Science and technology studies0.0040.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.009
GPT teacher head0.250
Teacher spread0.242 · 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 designCase report
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

Citations0
Published2006
Admission routes3
Has abstractyes

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