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2012· article· en· W2324452045 on OpenAlexaff
Derek J. Roberts, Vikas P. Chaubey, David Zygun, Diane Lorenzetti, Peter Faris, Chad G. Ball, Andrew W. Kirkpatrick, Matthew James

Bibliographic record

VenueCritical Care Medicine · 2012
Typearticle
Languageen
FieldMedicine
TopicIntracranial Aneurysms: Treatment and Complications
Canadian institutionsFoothills Medical CentreUniversity of Calgary
Fundersnot available
KeywordsMedicineDigital subtraction angiographyBlunt traumaRadiologyBluntComputed tomography angiographyMeta-analysisComputed tomographic angiographyLikelihood ratios in diagnostic testingAngiographyNuclear medicineDiagnostic accuracyInternal medicine

Abstract

fetched live from OpenAlex

Introduction: Blunt trauma to the carotid and/or vertebral arteries, collectively termed blunt cerebrovascular injury (BCVI), occurs in approximately 1% of hospitalized blunt trauma victims. Although computed tomographic angiography (CTA) is the most frequently used BCVI imaging test, controversy exists as to whether its diagnostic performance compares favorably with the reference-standard, digital subtraction angiography (DSA). Hypothesis: The diagnostic accuracy of CTA compares unfavorably with DSA for BCVI detection in trauma patients. Methods: We searched electronic databases (MEDLINE, PubMed, EMBASE, Cochrane, and Web of Science) (1950 to May 22nd, 2012), article bibliographies, conference proceedings (2008 to 2011), and clinical trials registries. Two investigators independently screened articles and selected studies comparing the accuracy of CTA with DSA for BCVI detection in trauma patients. Pooled estimates of sensitivity, specificity, and positive and negative likelihood ratios were calculated using bivariate random effects models. Results: Eight studies that examined 5704 carotid or vertebral arteries in 1426 trauma patients met inclusion criteria. The pooled sensitivity and specificity for BCVI detection with CTA versus DSA was 66% (95% CI, 49% to 79%; I2=80.4%) and 97% (95% CI, 91% to 99%; I2=94.6%), respectively. Corresponding pooled positive and negative likelihood ratios were 20.0 (95% CI, 6.9 to 58.4; I2=87.7%) and 0.35 (95% CI, 0.22 to 0.56; I2=74.9%), respectively. Although the pooled sensitivity varied with the number of available CT slices, the training of interpreting radiologists, and in a pattern suggestive of differences in diagnostic threshold for judging CTA positivity, it remained =80% among studies that used scanners with =16-slices per rotation and where the CTA was read by neuroradiologists. Conclusions: Existing evidence suggests that the diagnostic performance of CTA varies considerably across studies, likely due to an implicit variation in diagnostic threshold across trauma centers. Moreover, although CTA appears to lack sensitivity to adequately rule-out BCVI, it may be useful to rule-in BCVI among trauma patients with a high pretest probability of injury.

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)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.330
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0050.003
Open science0.0020.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.6700.510

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 head0.035
GPT teacher head0.341
Teacher spread0.307 · 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.

Study designNot applicable
Domainnot available
GenreOther

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
Published2012
Admission routes1
Has abstractyes

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