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Record W2789140310 · doi:10.21037/qims.2018.01.06

Gadofosveset-enhanced magnetic resonance imaging as a problem-solving tool for diagnosing colorectal liver metastases: a case report

2018· article· en· W2789140310 on OpenAlexaff
Helen Cheung, Paul J. Karanicolas, Natalie G. Coburn, Calvin Law, Laurent Milot

Bibliographic record

VenueQuantitative Imaging in Medicine and Surgery · 2018
Typearticle
Languageen
FieldMedicine
TopicHepatocellular Carcinoma Treatment and Prognosis
Canadian institutionsHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineMagnetic resonance imagingRadiologyColorectal cancerContrast (vision)CancerInternal medicine

Abstract

fetched live from OpenAlex

Due to significant improvements in morbidity and mortality, surgery with curative intent is now the standard of care for patients with colorectal liver metastases who are surgical candidates. As a result, highly accurate preoperative per-lesion diagnosis is crucial. However, in some instances, this remains limited with standard techniques, including magnetic resonance imaging (MRI) with conventional contrast agents. Delayed retention of contrast of fibrotic liver metastases on MRI with extracellular contrast agents may mimic the late retention of contrast in hemangiomas and represent a diagnostic pitfall that limits diagnosis. Early preliminary work suggests that this imaging pitfall may not be seen on MRI with intravascular contrast agents (e.g., gadofosveset). This case report describes a surgical patient with colorectal liver metastases where gadofosveset-enhanced liver MRI was helpful in determining patient 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 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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.448
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.081
GPT teacher head0.326
Teacher spread0.245 · 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.

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

Citations4
Published2018
Admission routes1
Has abstractyes

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