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Unveiling the unreal: Comprehensive imaging review of hepatic pseudolesions

2021· review· en· W3201356411 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueClinical Imaging · 2021
Typereview
Languageen
FieldMedicine
TopicLiver Disease Diagnosis and Treatment
Canadian institutionsUniversity of British ColumbiaDr. Everett Chalmers Regional Hospital
Fundersnot available
KeywordsMedicineRadiologyHepatic DiseasesParenchymaHepatic veinsMuscle hypertrophyFocal nodular hyperplasiaFibrosisLiver parenchymaHepatic fibrosisPathologyPortal veinCardiologyHepatocellular carcinomaInternal medicine

Abstract

fetched live from OpenAlex

Hepatic pseudolesions are defined as non-neoplastic focal abnormalities of the liver which can mimic or conceal true liver lesions. It is particularly common in liver due to its unique dual blood supply and the existence of multilevel anastomosis between them. Because of the recent advances in CT and MRI technology, they are being increasingly encountered in daily practice. Broadly they can be categorised in to (1) Focal parenchymal abnormalities like focal fatty change, focal fat sparing, focal confluent fibrosis, segmental hypertrophy and regenerative nodules, (2) Perfusion abnormalities which include transient hepatic parenchymal enhancement in portal vein obstruction, third inflow, intrahepatic shunts, hepatic arterial occlusion and hepatic venous obstruction, (3) Imaging pitfalls like parenchymal compression, unenhanced vessels and pseudolipoma. It is essential for the radiologists to be familiar with the typical and atypical imaging features of pseudolesions to avoid mistaking them for sinister pathologies and also to avoid overlooking underlying hidden pathologies.

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.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.874
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.169
GPT teacher head0.504
Teacher spread0.335 · 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