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Record W2093100687 · doi:10.1102/1470-7330.2013.0025

MDCT of abdominopelvic oncologic emergencies

2013· review· en· W2093100687 on OpenAlexaff
Sree Harsha Tirumani, Vijayanadh Ojili, Gowthaman Gunabushanam, Kedar N. Chintapalli, John Ryan, Caroline Reinhold

Bibliographic record

VenueCancer Imaging · 2013
Typereview
Languageen
FieldMedicine
TopicIntraperitoneal and Appendiceal Malignancies
Canadian institutionsMcGill University Health CentreOttawa HospitalMontreal General Hospital
Fundersnot available
KeywordsMedicineMultidetector computed tomographyMalignancyRadiologyPresentation (obstetrics)Computed tomographyComplicationGeneral surgerySurgeryPathology

Abstract

fetched live from OpenAlex

Acute complications arising in abdominopelvic malignancies represent a unique subset of patients presenting to the emergency room. The acute presentation can be due to complications occurring in the tumor itself or visceral or vascular structures harboring the tumor. Multidetector computed tomography (MDCT) is the investigation of choice in the workup of these patients and enables appropriate and timely management. Management of the complication depends primarily on the extent of the underlying malignancy and the involvement of other viscera. The purpose of this article is to depict the imaging features of these complications on MDCT.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.003
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.101
GPT teacher head0.419
Teacher spread0.318 · 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.

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

Citations16
Published2013
Admission routes1
Has abstractyes

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