MétaCan
Menu
Back to cohort

Mimics of pancreatic neoplasms at cross-sectional imaging: Pearls for characterization and diagnostic work-up

2024· review· en· W4405237315 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

VenueCurrent Problems in Diagnostic Radiology · 2024
Typereview
Languageen
FieldMedicine
TopicPancreatic and Hepatic Oncology Research
Canadian institutionsQueen Elizabeth II Health Sciences Centre
Fundersnot available
KeywordsMedicinePancreasPancreatitisDifferential diagnosisRadiologyAccessory spleenIntraductal papillary mucinous neoplasmPathologyNeoplasmAcute pancreatitisInternal medicineSpleenSplenectomy

Abstract

fetched live from OpenAlex

Interpreting imaging examinations of the pancreas can be a challenge. Several different entities can mimic or mask pancreatic neoplasms, including normal anatomic variants, non-pancreatic lesions, and both acute and chronic pancreatitis. It is important to distinguish these entities from pancreatic neoplasms, as the management and prognosis of a pancreatic neoplasm, particularly adenocarcinoma, have considerable impact on patients. Normal pancreatic variants that mimic a focal lesion include focal fatty atrophy, annular pancreas, and ectopic pancreas. Extra-pancreatic lesions that can mimic a primary pancreatic neoplasm include vascular lesions, such as arteriovenous malformations and pseudoaneurysms, duodenal diverticula, and intra-pancreatic accessory spleen. Both acute and chronic pancreatitis can mimic or mask a pancreatic neoplasm and are also associated with pancreatic ductal adenocarcinoma. Awareness of these entities and their imaging features will enable the radiologist to narrow the differential diagnosis, provide recommendations that expedite diagnosis and avoid unnecessary work-up or delays in patient care.

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)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.698
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.086
GPT teacher head0.414
Teacher spread0.328 · 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