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Record W2062508719 · doi:10.12659/pjr.891114

CT Angiography in the Diagnosis of Brain Death

2014· review· en· W2062508719 on OpenAlexaboutno aff
Jerzy Walecki

Bibliographic record

VenuePolish Journal of Radiology · 2014
Typereview
Languageen
FieldMedicine
TopicOrgan Donation and Transplantation
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAngiographyComa (optics)RadiologyRadionuclide angiographyCerebral angiographyCerebral perfusion pressurePerfusion scanningTranscranial DopplerPerfusionCardiologyHeart failure

Abstract

fetched live from OpenAlex

Summary Brain death is defined as the irreversible cessation of functioning of the entire brain, including the brainstem. Brain death is principally established using clinical criteria including coma, absence of brainstem reflexes and loss of central drive to breathe assessed with apnea test. In situations in which clinical testing cannot be performed or when uncertainty exists about the reliability of its parts due to confounding conditions ancillary tests (i.a. imaging studies) may be useful. The objective of ancillary tests in the diagnosis of brain death is to demonstrate the absence of cerebral electrical activity (EEG and evoked potentials) or cerebral circulatory arrest. In clinical practice catheter cerebral angiography, perfusion scintigraphy, transcranial Doppler sonography, CT angiography and MR angiography are used. Other methods, like perfusion CT, xenon CT, MR spectroscopy, diffusion weighted MRI and functional MRI are being studied as potentially useful in the diagnosis of brain death. CT angiography has recently attracted attention as a promising alternative to catheter angiography - a reference test in the diagnosis of brain death. Since 1998 several major studies were published and national guidelines were introduced in several countries (e.g. in France, Austria, Switzerland, the Netherlands and Canada). This paper reviews technique, characteristic findings and criteria for the diagnosis of cerebral circulatory arrest in CT angiography.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.932
Threshold uncertainty score0.321

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
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 head0.049
GPT teacher head0.364
Teacher spread0.315 · 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 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

Citations25
Published2014
Admission routes1
Has abstractyes

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